{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "# default_exp problems_chapter_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 2 - PROBLEMS\n",
    "\n",
    "> Fundamentals of Supply Chain Theory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "from nbdev.showdoc import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 2.1 (Forecasting without Trend)\n",
    "\n",
    "A hospital receives regular shipments of liquefied oxygen, which it converts to oxygen gas that is used for life support. The company that sells the oxygen to the hospital wishes to forecast the amount of liquefied oxygen the hospital will use tomorrow. The number of liters of liquefied oxygen used by the hospital in each of the past 30 days is reported in the file oxygen.xlsx.\n",
    "\n",
    "\n",
    "1. Using a moving average with $N=7$ , forecast tomorrow's demand.\n",
    "2. Using single exponential smoothing with $\\alpha=0.1$, forecast tomorrow's demand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Day'>"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#Load data\n",
    "df = pd.read_excel('../data/oxygen.xlsx',index_col='Day')\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1.1 Using a moving average with $N=7$ , forecast tomorrow's demand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2918.2571428571428"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#set window value\n",
    "N = 7\n",
    "\n",
    "#Moving Average\n",
    "df['MA'] = df['Demand'].rolling(window=N).mean()\n",
    "\n",
    "#Result\n",
    "float(df.tail(1)['MA'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1.2 Using single exponential smoothing with $\\alpha=0.1$, forecast tomorrow's demand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2841.5788123727452"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SES'] = df['Demand'].ewm(alpha=0.1, adjust=False).mean()\n",
    "float(df.tail(1)['SES'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 (Forecasting with Trend)\n",
    "\n",
    "The demand for a new brand of dog food has been steadily rising at the local PetMart pet store. The previous 26 weeks' worth of demand (number of bags) are given in the file dog‐food.xlsx.\n",
    "\n",
    "1. Using double exponential smoothing with $\\alpha=0.2$ and $\\beta=0.1$ , forecast next week's demand. Initialize your forecast by setting images for images and images .\n",
    "\n",
    "2. Using linear regression, forecast next week's demand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Week'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#Load data\n",
    "df = pd.read_excel('../data/dog-food.xlsx', index_col='Week')\n",
    "\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2.1. Using double exponential smoothing with $\\alpha=0.2$ and $\\beta=0.1$ , forecast next week's demand. Initialize your forecast by setting $I_t = D_t $ for $t=1,2$ and $S_2 = I_2 - I_1$ ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.api import Holt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Demand</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Demand\n",
       "Week        \n",
       "1        646"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:578: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  warnings.warn('An unsupported index was provided and will be'\n",
      "/opt/conda/lib/python3.8/site-packages/statsmodels/tsa/holtwinters/model.py:427: FutureWarning: After 0.13 initialization must be handled at model creation\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holt = Holt(df['Demand'], initialization_method=None)\n",
    "\n",
    "#Initialization_method equal to None corresponds to  (signal) I_1 = D_1 and (slope) S_1 = D_2 - D_1\n",
    "I_1, S_1 = df['Demand'].iloc[0], df['Demand'].iloc[1] - df['Demand'].iloc[0]\n",
    "holt.initial_values()[:-1] == (I_1, S_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "fit = holt.fit(\n",
    "    smoothing_level=0.2,\n",
    "    smoothing_trend=0.1,\n",
    "    optimized=False,\n",
    ")\n",
    "\n",
    "df['Holt'] = fit.fittedvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:376: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.\n",
      "  warnings.warn('No supported index is available.'\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "30    1780.346625\n",
       "Name: Holt's linear trend, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fcast = fit.forecast(5).rename(\"Holt's linear trend\")\n",
    "\n",
    "#result\n",
    "fcast.tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Week'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Demand'].plot(marker='o', color='black', legend=True)\n",
    "df['Holt'].plot(color='blue', legend=True)\n",
    "fcast.plot(marker='o', color='green', legend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " #### 2.2.2. Using linear regression, forecast next week's demand. Using linear regression, forecast next week's demand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.api import OLS, add_constant #Ordinary Least Squares\n",
    "#from statsmodels.regression.linear_modeimport OLS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                 Demand   R-squared:                       0.997\n",
      "Model:                            OLS   Adj. R-squared:                  0.996\n",
      "Method:                 Least Squares   F-statistic:                     6896.\n",
      "Date:                Sun, 01 Aug 2021   Prob (F-statistic):           4.89e-31\n",
      "Time:                        00:01:30   Log-Likelihood:                -110.29\n",
      "No. Observations:                  26   AIC:                             224.6\n",
      "Df Residuals:                      24   BIC:                             227.1\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const        600.1169      7.072     84.853      0.000     585.520     614.714\n",
      "x1            38.0284      0.458     83.039      0.000      37.083      38.974\n",
      "==============================================================================\n",
      "Omnibus:                        0.789   Durbin-Watson:                   0.512\n",
      "Prob(Omnibus):                  0.674   Jarque-Bera (JB):                0.731\n",
      "Skew:                           0.100   Prob(JB):                        0.694\n",
      "Kurtosis:                       2.203   Cond. No.                         31.9\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "model = OLS(df['Demand'], add_constant(df.index))\n",
    "results = model.fit()\n",
    "df['OLS_fitted'] = results.fittedvalues\n",
    "pred_df = pd.DataFrame(index=pd.RangeIndex(27, 31, name='Week'))\n",
    "pred_df['OLS_prediction'] = results.predict(add_constant(pred_df.index))\n",
    "print(results.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OLS_prediction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1626.883077</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      OLS_prediction\n",
       "Week                \n",
       "27       1626.883077"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Result\n",
    "pred_df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Week'>"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Demand'].plot(marker='o', color='black', legend=True)\n",
    "df['OLS_fitted'].plot(color='blue', legend=True)\n",
    "\n",
    "pred_df['OLS_prediction'].plot(marker='o', color='green', legend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3 (Forecasting Cupcake Sales) Karl's Cupcakes recently launched a new variety of cupcake. The weekly demands, measured in dozens, during the first two weeks of sales were $D_1 = 47.2$ and  $D_2 = 52.3$.\n",
    "\n",
    "1. Use double exponential smoothing with $\\alpha=0.1$ and $\\beta=0.2$ to calculate $y_3$, the forecast made in week 2 for the demand in week 3.\n",
    "\n",
    "2. Suppose the actual demand in week 3 is 59.4. What is $y_4$ , the forecast made in week 3 for the demand in week 4?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Demand</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Demand\n",
       "Week        \n",
       "1       47.2\n",
       "2       52.3"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from statsmodels.tsa.api import Holt\n",
    "\n",
    "D_1, D_2 = 47.2, 52.3\n",
    "df = pd.DataFrame({'Demand': [D_1, D_2]})\n",
    "df.index = pd.RangeIndex(start=1, stop=3, name='Week')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/statsmodels/tsa/holtwinters/model.py:427: FutureWarning: After 0.13 initialization must be handled at model creation\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holt = Holt(df['Demand'], initialization_method=None)\n",
    "\n",
    "#Initialization_method equal to None corresponds to  (signal) I_1 = D_1 and (slope) S_1 = D_2 - D_1\n",
    "I_1, S_1 = df['Demand'].iloc[0], df['Demand'].iloc[1] - df['Demand'].iloc[0]\n",
    "holt.initial_values()[:-1] == (I_1, S_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "fit = holt.fit(\n",
    "    smoothing_level=0.2,\n",
    "    smoothing_trend=0.1,\n",
    "    optimized=False,\n",
    ")\n",
    "\n",
    "df['Holt'] = fit.fittedvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    65.31928\n",
       "Name: Holt's linear trend, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fcast = fit.forecast(2).rename(\"Holt's linear trend\")\n",
    "\n",
    "#result\n",
    "fcast.tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Week'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Demand'].plot(marker='o', color='black', legend=True)\n",
    "df['Holt'].plot(color='blue', legend=True)\n",
    "fcast.plot(marker='o', color='green', legend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Suppose the actual demand in week 3 is 59.4. What is $y_4$ , the forecast made in week 3 for the demand in week 4?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    69.997518\n",
       "Name: Holt's linear trend, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D_1, D_2, D_3 = 47.2, 52.3, 59.4\n",
    "df = pd.DataFrame({'Demand': [D_1, D_2, D_3]})\n",
    "df.index = pd.RangeIndex(start=1, stop=4, name='Week')\n",
    "\n",
    "holt = Holt(df['Demand'], initialization_method=None)\n",
    "\n",
    "fit = holt.fit(\n",
    "    smoothing_level=0.2,\n",
    "    smoothing_trend=0.1,\n",
    "    optimized=False,\n",
    ")\n",
    "\n",
    "fcast = fit.forecast(2).rename(\"Holt's linear trend\")\n",
    "\n",
    "#result\n",
    "fcast.tail(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4 (Forecasting with Seasonality) \n",
    "\n",
    "A hardware store sells potting soil, the demand for which is highly seasonal and has also exhibited a slight upward trend. The number of bags of soil sold each month for the past 40 months is reported in the file potting‐soil.xlsx. Using triple exponential smoothing with $\\alpha = 0.2$ , $\\beta=0.1$ , and $\\gamma =0.3$ , forecast the demand for May. Initialize your forecast by setting  equation for periods\n",
    "\n",
    "$$ I_t =  D_t \\\\\n",
    "S_t = I_t - I_{t-1} \\\\\n",
    "c_t = \\frac{12 D_t}{\\sum^{12}_{i=1}D_i}\n",
    "$$\n",
    "\n",
    "\n",
    ". (There are better ways to initialize this method, but this method is simpler.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from statsmodels.tsa.api import ExponentialSmoothing\n",
    "\n",
    "#Load data\n",
    "df = pd.read_excel(\n",
    "    '../data/potting-soil.xlsx',\n",
    "    index_col='Month',\n",
    ")\n",
    "\n",
    "df.drop(\n",
    "    columns=['Name'],\n",
    "    inplace=True,\n",
    ")\n",
    "\n",
    "\n",
    "df.index = pd.date_range(\n",
    "    '2019-01-01',\n",
    "    freq='M',\n",
    "    periods=len(df),\n",
    "    name='Month',\n",
    ")\n",
    "\n",
    "df.plot(marker='o');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "exponential_smoothing = ExponentialSmoothing(\n",
    "    df['Demand'],\n",
    "    seasonal_periods=12,\n",
    "    seasonal='add',\n",
    "    trend='add',\n",
    "    freq='M',\n",
    "    initial_level=df['Demand'].iloc[0],\n",
    "    initial_trend=df['Demand'].iloc[1] - df['Demand'].iloc[0],\n",
    "    initial_seasonal=12 * df['Demand'][:12] / df['Demand'][:12].sum(),\n",
    "    initialization_method='known',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "#exponential_smoothing.fit?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "fit = exponential_smoothing.fit(\n",
    "    smoothing_level=0.2,\n",
    "    smoothing_trend=0.1,\n",
    "    smoothing_seasonal=0.3,\n",
    ")\n",
    "\n",
    "df['HoltWinters'] = fit.fittedvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Month'>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Demand'].plot(marker='o', color='black', legend=True)\n",
    "df['HoltWinters'].plot(color='blue', legend=True)\n",
    "#fcast.plot(marker='o', color='green', legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2022-05-31    1218.725201\n",
       "Freq: M, Name: Holt-Winter's forecast, dtype: float64"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fcast = fit.forecast(1).rename(\"Holt-Winter's forecast\")\n",
    "\n",
    "#result\n",
    "fcast.tail(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.5 (Forecasting Melon Slicers)\n",
    "\n",
    "Matt's Melon Slicers sells specialized knives for watermelons, the demand for which is highly seasonal, with the majority of the demand occurring during the summer. The company has been selling melon slicers for three years and has calculated the following estimates of the seasonal factors, with each period representing one quarter:\n",
    "\n",
    "|Quarter | $t$ | $c_t$|\n",
    "--- | --- | ---\n",
    "|Winter|9|0.4|\n",
    "|Spring|10|0.8|\n",
    "|Summer|11|1.9|\n",
    "|Fall|12|0.9|\n",
    "\n",
    "At the end of period 12, the company calculated the following estimates of the base signal and slope: $I_{12}=642$ , $S_{12}=84$.\n",
    "\n",
    "1. Calculate $y_{13}$ , the forecast made in period 12 for the demand in period 13.\n",
    "\n",
    "2. Suppose the demand in period 13 turns out to be 341. Calculate $I_{13}$, $S_{13}$ and $c_{13}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.5.1. Calculate $y_{13}$ , the forecast made in period 12 for the demand in period 13."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One has\n",
    "$$ y_t  =  I_{t-1} + S_{t - 1} $$\n",
    "\n",
    "therefore\n",
    "\n",
    "\n",
    "$$ y_{13}  =  642  - 84 = 558 $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.5.2 Suppose the demand in period 13 turns out to be 341. Calculate $I_{13}$, $S_{13}$ and $c_{13}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From \n",
    "\n",
    "$$ I_t = \\alpha D_t + (1 - \\alpha) (I_{t-1} + S_{t-1})  $$\n",
    "\n",
    "$$ S_t  = \\beta (I_t - I_{t-1}) + (1 - \\beta)  S_{t-1}$$\n",
    "\n",
    "one has \n",
    "\n",
    "\n",
    "$$ I_{13} f= "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.6 (Forecasting Using Regression) \n",
    "The demand for bottled water at football (aka soccer) matches is correlated to the outside temperature at the start of the match. The file bottled‐water.xlsx reports the temperature (∘C) and number of bottles of water sold for each home match played at a certain stadium for the past two seasons (19 home matches per season).\n",
    "\n",
    "1. Using these data, build a linear regression model to relate the demand for bottled water to the match‐time temperature. What are $\\beta_{0}$ and $\\beta_1$ ?\n",
    "\n",
    "2. The temperatures for the next three matches are predicted to be 21.6 C, 27.3, and 26.6, respectively. Forecast the demand for bottled water at each of these matches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#Load data\n",
    "df = pd.read_excel(\n",
    "    '../data/bottled-water.xlsx',\n",
    "    index_col='Match #',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.6.1. Using these data, build a linear regression model to relate the demand for bottled water to the match‐time temperature. What are $\\beta_{0}$ and $\\beta_1$ ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.api import OLS, add_constant "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                 Demand   R-squared:                       1.000\n",
      "Model:                            OLS   Adj. R-squared:                  1.000\n",
      "Method:                 Least Squares   F-statistic:                 1.525e+06\n",
      "Date:                Tue, 03 Aug 2021   Prob (F-statistic):           6.88e-85\n",
      "Time:                        18:18:08   Log-Likelihood:                -2.7124\n",
      "No. Observations:                  38   AIC:                             9.425\n",
      "Df Residuals:                      36   BIC:                             12.70\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const        200.7325      0.409    491.046      0.000     199.903     201.562\n",
      "Temp          18.9773      0.015   1234.731      0.000      18.946      19.008\n",
      "==============================================================================\n",
      "Omnibus:                        2.901   Durbin-Watson:                   1.820\n",
      "Prob(Omnibus):                  0.234   Jarque-Bera (JB):                2.636\n",
      "Skew:                          -0.573   Prob(JB):                        0.268\n",
      "Kurtosis:                       2.407   Cond. No.                         251.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/statsmodels/tsa/tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
      "  x = pd.concat(x[::order], 1)\n"
     ]
    }
   ],
   "source": [
    "model = OLS(df['Demand'], add_constant(df['Temp']))\n",
    "results = model.fit()\n",
    "df['OLS'] = results.fittedvalues\n",
    "\n",
    "print(results.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "beta_0:  200.73250873778298\n",
      "beta_1:  18.97727827658134\n"
     ]
    }
   ],
   "source": [
    "print(\"beta_0: \", results.params[0])\n",
    "print(\"beta_1: \", results.params[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "ax.plot(df['Temp'], df['Demand'], 'o', label=\"data\")\n",
    "ax.plot(df['Temp'], df['OLS'], 'r--.', label=\"OLS\")\n",
    "ax.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. The temperatures for the next three matches are predicted to be 21.6 C, 27.3 C, and 26.6 C, respectively. Forecast the demand for bottled water at each of these matches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/statsmodels/tsa/tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
      "  x = pd.concat(x[::order], 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Match #\n",
       "39    610.641720\n",
       "40    718.812206\n",
       "41    705.528111\n",
       "Name: OLS, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = pd.DataFrame({'Temp': [21.6, 27.3, 26.6]},\n",
    "                    index=pd.RangeIndex(39, 42, name='Match #'))\n",
    "#df = pd.concat([df, pred])\n",
    "pred['OLS'] = results.predict(add_constant(pred['Temp']))\n",
    "pred['OLS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "ax.plot(df['Temp'], df['Demand'], 'o', label=\"data\")\n",
    "ax.plot(pred['Temp'], pred['OLS'], 'o', label=\"OLS\")\n",
    "ax.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.8 (Forecasting using Machine Learning Methods) \n",
    "Using the data set provided in Problem 2.6, choose a learning‐based forecasting method—a tree‐based model, SVR, or neural networks—for forecasting bottled water given temperatures. Use your selected method to forecast the demand during matches when the temperatures are $21.6$ , $27.3$ , and $26.6$. Compare your results with those you obtained using linear regression in Problem 2.6(b)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn import svm\n",
    "\n",
    "#Load data\n",
    "df = pd.read_excel(\n",
    "    '../data/bottled-water.xlsx',\n",
    "    index_col='Match #',\n",
    ")\n",
    "regr = svm.SVR()\n",
    "regr.fit(\n",
    "    X=df['Temp'].to_numpy().reshape(-1, 1),\n",
    "    y=df['Demand'],\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "regr.predict([[21.6], [27.3], [26.6]])\n",
    "\n",
    "pred = pd.DataFrame({'Temp': [21.6, 27.3, 26.6]},\n",
    "                    index=pd.RangeIndex(39, 42, name='Match #'))\n",
    "\n",
    "pred['SVR'] = regr.predict(pred['Temp'].to_numpy().reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temp</th>\n",
       "      <th>SVR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Match #</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>21.6</td>\n",
       "      <td>698.516457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>27.3</td>\n",
       "      <td>710.808279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>26.6</td>\n",
       "      <td>706.343984</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Temp         SVR\n",
       "Match #                  \n",
       "39       21.6  698.516457\n",
       "40       27.3  710.808279\n",
       "41       26.6  706.343984"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "ax.plot(df['Temp'], df['Demand'], 'o', label=\"data\")\n",
    "ax.plot(pred['Temp'], pred['SVR'], 'o', label=\"OLS\")\n",
    "ax.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.10 (Forecasting Fires) \n",
    "\n",
    "The file nyc‐fires.csv contains the number of fires responded to by the New York City Fire Department on each day from January 1, 2013 through June 30, 2016 (NYC OpenData, 2017). It also contains the high temperature (in ∘F) and the total precipitation (in inches) on the same days (National Oceanic and Atmospheric Administration (NOAA), 2017).\n",
    "Load the data into MATLAB, Excel, or another software package of your choice. Add a variable called IsWeekend that indicates whether each day is a weekend day (Saturday or Sunday). Split the data into two parts, one for 2013–2015 (this will be your training data) and one for 2016 (this will be your testing data).\n",
    "\n",
    "In this problem, you will build models to predict the number of fires on a given day using the three features (high temperature, precipitation, and weekend (Y/N)). Use only the training data when building your models.\n",
    "\n",
    "1. Build a linear regression model. Report the coefficients $\\hat{\\beta}_i$ .\n",
    "2. Build a regression tree model with at most 10 branching nodes. (A branching node is a node that has child nodes.) Include a diagram of your tree.\n",
    "3. Build an SVR model. Report the coefficients images and images .\n",
    "4. For each method in parts (a)–(c), predict the number of fires on each day in the testing data. Report the predicted and actual values and the forecast error for the first 10 records in the testing data. Also report the MSE for each method for the entire testing set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.11 (Exponential Smoothing for Retail Sales) \n",
    "The file retail‐sales‐data.csv contains weekly sales data for 99 departments within 45 retail stores over approximately 3 years. This is actual data from a real company but has been anonymized (see Kaggle.com (2017)).\n",
    "\n",
    "1. Extract the sales data for store 2, department 93. Determine the most appropriate form of exponential smoothing (single, double, or triple) and apply that method to forecast the sales. Use 0.15 for all of the smoothing constants ($\\alpha$ , $\\beta$, and/or $\\gamma$ ). Begin forecasting at the earliest period you can. (For example, in double exponential smoothing the forecasts begin in period 3.) Report the MSE, MAD, and MAPE for your forecasts. Plot the actual and forecast sales on a single plot.\n",
    "\n",
    "2. Repeat part (a) for store 3, department 60.\n",
    "3. Repeat part (a) for store 1, department 16."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import svm\n",
    "from statsmodels.tsa.api import ExponentialSmoothing\n",
    "from statsmodels.tools import eval_measures\n",
    "\n",
    "\n",
    "#Load data\n",
    "df = pd.read_csv(\n",
    "    '../data/retail_sales_data.csv',\n",
    "    index_col='Date',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "ex = np.array([1,2,1,0])\n",
    "l =  ex != 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0])"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_e = np.full_like(ex,0)\n",
    "p_e.flat[l]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.11.1 Extract the sales data for store 2, department 93. Determine the most appropriate form of exponential smoothing (single, double, or triple) and apply that method to forecast the sales. Use 0.15 for all of the smoothing constants ($\\alpha$ , $\\beta$, and/or $\\gamma$ ). Begin forecasting at the earliest period you can. (For example, in double exponential smoothing the forecasts begin in period 3.) Report the MSE, MAD, and MAPE for your forecasts. Plot the actual and forecast sales on a single plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_93_2 = df[(df['Dept'] == 93) & (df['Store'] == 2)]\n",
    "df_93_2.index = pd.to_datetime(df_93_2.index)\n",
    "\n",
    "dff = df_93_2.resample('W').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff.shape == df_93_2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LpLRIpwpqxYEyXl93lKhgf66Ylsrs4TG8vyWf+z/cRYCfDx/cMZsHzxuDj48wX3PplBTOHBPP377db3YkKqq1VlCTUiOYNzqOZ5cdtDuCeXnVIZ5dfpArpqXy2MLxCCEYp9cjSzPfqpwKfATMGR5rDjsnM4GtedVU2JjTNx6pYkpaJIF+BgCGxoYwNT2Sj7cVetwq0R8KKg+YJYQI1ueSzgL2Aj8CV+hxbgA+078v1f9H//0HqeV6KbBI9/IbCowENgGbgZG6V6A/miPF0n7Il9fQ0t7JopfX8/N3tg50Ulzi7Q3H8BGCX5wxwio8PTrYroJ6ZfVhcssamJ4RBUBcmNag2drnTWtqvrTTm8yvaiYiyI+IID9CAnxZMD6RL3YVdWl8bCmqaeYLveHbfNS1EZcnqW9p59dLtvPj/jKr8Pe3FNDQ2sHNpwzl0ikpFNY0s8XFEWF37Cio4WhlE1/uKmZPoWOnEqNR8vG2Apfm85rbOtmeX83sYTFdfksI1+ZTSuu08vxWX7j7i3kj+PTOudypvyeRwZpiM213tP5QJec9t5qdBTXEhQXw8qpDThvI9zbn8fN3tpKZHM645HC22FkcfLSikeX7ypicFsmktAg2Hqli3SHrDs+RikZC/A3m9xBgzvAYdhfUOHTPfmvDMRLCA3jzlhk8cvE4/n3tVLb8/mzevW0mX/3qVLL00YglQgieuHQCfgYfHvhoF1JK8y4SKbqCEkLw6MXj6TBKHv0i2+r6svoW/vLNAc4bn8hfLp9oVn5DY0MI8jNYOQytzilnUlokEcHHOw/nZCYgJfxg8e7VtbSTXVTLTJtyvGxqKgdK61mb69k1Yf0xB7URzdlhG7Bbl/ky8BvgHiFELtoc06v6Ja8CMXr4PcCD+n2ygffRlNs3wJ1Syk59nuou4FtgH/C+HndQYDRK7nl/BxsOV/Hd3tI+sQN7kua2Tt7bnM+C8YnmhslEmh0F9fraIzz+5T7On5DILacMBewrqNaOTnMj/l12qZXdHrQRVHp0sPn/y6amUN/SYVX57PGGbl4J9POx26CZkFLyu092c/rffuSy/6zloY93OZw0z69qcmm009TWwc2vb+azHUW8YOEl1mmUvL7uCFlDopiYGsn8cQkE+xs8Zub7encxfgZBRJAfzyw76DDe1rxq7nl/J88tz+n2nluPVdPeKZk13JmC0t7dnNIGYkMDuO/c0UxOizQ3rNFmE5+mBP79Yy7Bfga++OUp3H32SPYU1rHxiP0yWp1Tzm8/2cOpI+NYfNss5o2OY09RHY02ZkVT5+apyyfyz2umEhbgy7d7rM1cRysbGRITgtbf1pg9PAajhM125B+rbGTlwXKumZGOn+F4kxsW6Mec4bHmkYg9EiMCeei8sWw6UsU3e0oorGkhJsTf6pr0mGB+ddZIvtpdYtWR+WRbIZ1GyX3njsZgMTIz+AjGJoWRXaiNoGqb2tmZX8OpI62tSplJ4aREBlnNQ209Wo1Rwqyh1gr18qmpDI0N4YEPd7q8hsoV+sWLT0r5RynlGCnleCnl9bon3mEp5Qwp5Qgp5ZVSylY9bov+/wj998MW9/mzlHK4lHK0lPJri/CvpJSj9N/+3B958hb+9t0BvtpdwnWz0pHS/ujBm/hsRyF1LR3cMCejy29DYoIprG42r79YdbCcRz7fy7njEnhu0RR89codb1JQFqaH9YcqaWjt4LIpKVQ0tLItz3o0kW+joOYMjyUhPICPtzleZNjQ2sG7m/I4b3wi0zOi2eSg8QNNkb2zMY+0qGD8DD4s3pTPhzb3Lqtv4VeLt3PqX3/ktbVH7N7n1TVH+M2Hu3hl9WFuf3MrW49VMyMjmi1Hq6jU87smt4L8qmZunJsBQLC/LwvGJfKlCyPC7pBS8tXuEk4dGcftpw3jh/1lDufqdhVoPfDX1x3ttmO0/nAFBh9hnrewxFSephFUTlkDI+NDu8SLDtZNfPoI6kBpPXNGxDIsLpTLp6YSHeLPK6sPd7kut6yeX7yzjZHxofznJ1MJ9DMwPSOaTqNke16NVdyvdhczJT2SlMgg/H19OGNMPMv3WXd4jlQ0MjQuxOq6qelR+Pv62DXzvb3hGAYhuGZGurNH5JCrslIZGR/KX789QF5Vo9m8Z8ltpw5jeFwIf/o8m7YOI1JKPtxawNT0SPMcniXjkiPYW1yH0Sj5dEchRtl1blAIwdlj41mdU27ubG04UomfQTAlPcoqbpC/gWeumkRpfSuPLPXc+EDtJOGl/HLxdu55f4dT19V9xXW8sOIQ18xI47GF4xmXHM7nO+3b1fsTR2YWKSVvrD/G2KRwsoZEdfk9PTqYDqM0eyStPFhOoJ8Pz18zxarnGRvadQT13d5Sgv0N/O6CsfgbfPhmz/F1GZ1GSUF1M2kWCsrgI7hkcgorDpSbG34TRqOkpb2Tdzceo76lg1tPHUbWkGgOlNbb3QYmu6iWJ77az1lj4nnrlhksuX0WE1Mj+N/aI+aR0uqccs5+eiXf7CkhLiyAD+0slFx1sJzHvtjL57uKePzLfazJreAvl0/kDxdlYpTwvd47/mhrARFBfpyTmWC+9tKpKdS1dDhdj5Jf1cR9H+zk5tc3O1RkuwpqKaxp5vwJSdw4J4OYEH+edTCKyi6sJTzQF6OUPPe981HU+kOVTEyNIDTAt8tvIQG+hAX4UlrXgpSS3LIGRiZ0bVQjdQVV3dhGTVMb5fWtjNLjBfoZuG7WEJbvK+OwhWt0R6eR297cSoCvD6/ckGWWP21IFD4Cqz0Aj1Q0kl1UxwUTksxh88clUNnYZh49t3UYKahuNnvwmQj0M5A1JKqLgmpu6+T9LQWcO66rxcBVfA0+/GbBGI5UNLI2t9Js3rPE39eH31+QydHKJt7ZeIydBbXklDVwZVaanTvCuORwGlo7OFhWz79/zGVGRrR5rs+SszMTaGk3sjpHcyrbeLiKiamRBPl3HfVNSY/izjNGeNTlXCmoXnK4vMGha3RP2VtUx+c7i/h4WyFXv7ye0roWSuta2HSkysre/9XuYnwE3Dd/NEIILpyYzI78GrPHWm+RUvLm+qN2F/XZo76lnStfXMfvP91j9/ctx6rZV1zHDbOHWJlHTJgUiCn9ewprGZsUToCvdWWICPLDzyDMCspolCzbW8q80XHEhAZw6shYvtlTYlaUpXUttHUarUZQoNnNO4zSSqlvOlLFmIe/YczD3/DEV/uZnhHF5LRIpg+NQkrtPCpLmts6+eXi7USF+PG3KychhEAIwU1zMzhc3siqnHKqGtv4v/d2kBAeyNd3n8ovzxzB/pJ6qy1qapvb+c1HuxgeF8K2h89h6+/PZv1DZ3JlVhrjksNJjghk2d5S6lra+Ta7hIsnJVs9l7nDYxkaG2Ll8WXJ098d4MynV7B0ZxE/7C/jqa/324331Z5ifH0E54xNICTAl9tOG8aa3AqrRt/EnqJapmdEc+2MdN7fkm83Dmhu1LsKapljx7xnIj48gLL6FoprW2ho7WBkQliXOP6+PoQG+FLd1M7BUk3WKIt4188agr+vD6+sOT46/fFAOUcqGnls4XhSo46Xf1igH2OTwq1MciZPuPMtFNS80fH4+/rwnW7myq9uotMozR58lswZHsO+4jorp4KlOwupbW7n+tlDHObdFc4aG2+eg7U3gtLSGsfcETE8930Or605QqCfDxdMTLIb1+Qo8ftP9lBW38q980fZrZMzh8YQFxbAI0uzOVBSz+7CWmYO7ToKNvHLM0cwMTXC3ew5ZFArqKKa5l5vtPjHpdksenkDL3loJTnAks15+Pv68PSVk8gta2DWk98z84nvueql9Tzx1fHV3d/sKWHG0Ghi9BHFhfrL+IUL3kzdIaXk8S/38YfPsrnjrS3d2pXbOoz8/O1tbD5azQdbC6i3E/+zHYWE+BtYONn+MjWTAjmmz9Fk61sb2SKEIC70+G4SOwpqKK9v5dxxiQCcOz6Rwppms5fScQ8+64o9OjGMzKRwPraYu3lj3VGCAww8sGA0v79gLH+/chIAU9Ki8PURbLaZh3p3Ux6Hyxv5+5WTzHMkABdMSCYuLID/rT3KH5dmU9vcznOLpjA8LpQLJiRh8BF8uv24Ynz0872U1bfy9FWTCfQzEBMaQFLE8Ynw+eMSWZ1TzodbCmjtMHKFxToiAB8fwQ2zh7A9r6ZLhyKvsol//pDLWWMSWHn/PG6eO5TX1x3t4kIspeTr3SXMHRFrniy/aFIygLmBNtHU1kFuWQPjUiK468yR+Bt8+POX++zOrW0+WkWHUTJ7WGyX30wkhAdSUttCju76bM/EB5g3jD2g11tLBRUXFsDlU1P5cEuBeT5r8aY84sMCONtitGliekY02/OrzeuBvtxVzNT0SCsFEBrgyykjYvk2W+vwmLzphtpRUCYZizfmAVrH6b+rjzA2Kdxpo+4KQggePG+sLjvYYZzfnj+W2uZ2lu4sYsG4xC4ekyZGJYbi6yPYcqyaU0bEdnF6MOHv68PrN02nsa2Ty/6zlk6jdBgXwM/gwzu3znQzd44ZtAqqtrmd+c+u4vL/rOvxpJ7RKNmRX0NogC9Pfr2fx7/Y63ATRhObj1bxzHcHHJrBmts6+WR7IeeNT+Tyaal8/Is53H7qMP508TjOHpvAR1sLqW1qJ7esgZyyBhbojTJoI5Ap6ZG9NvNJKfnbtwd4dc0RFoxLpLy+lb846HGb4v/mo12sya3g+llDaOswsnxf1/UTGw5XMX1otF3zAEBSRBC+PoK8qiaOVWlnP41Ptt8bs9xN4rvsUnx9BPNGxwNw9tgEDD7CbOYzKSjbERRozhK7CmrJLaunqrGN7/aWcNmUVH4xbwS3njqMIbopJ8jfwPiUCCsF1dZh5JXVh5k5NLrLBLO/rw/XzxrCyoPlfL6ziF+dOZLMZM2lPiY0gNNGxrJ0RyFGo+T9zfl8tK2AX8wbzmQ7ZhaA+bqp5e/fHWBEfKjdXurl01IJDfA1O3aY+GG/VhYPnjeGpIggfnPeaDKTwrn/w51WWwztLqwlr6qJ8yccf6dSIoOYkBLRxXS4r7geo4QJKdrWU/edO5rv95fx/A9dTX3L9pYS4OvDNDtmXROmxbo5uuJxqKCC/alqbCOntJ6wAF+SIqzNZj8/fTidUvLfVYcprGlmxYEyrspKszIRm5ieEU1Lu5E9hbVsy6tmb3EdF0xM7hJvfmYCBdXNfLe31GwStKegxiSGc8boOF5be4SmNs0BJ7esgZ+dPszu6MRdpg2J4stfneLQbAfayOjyqVrnxVm8AF8DI/RnfM/8UU7ljkuO4K1bZuCjr7lzVo6gjU49xaBVUHlVTaREBlHf2sGSTXk9useRykbqWzr43QVjuXFOBq+sOcK8v63g1TVHHLrevrTyMM//kMs2m8lZE1/tLqa+pYNF07UJ1TGJ4Tx0/lhumJPBPeeMorm9kyWb8/hmjzZKOnd8otX1F01MZm9xnUNziyv8sL+M/6w4xDUz0nnhuqncNHco72zMc+gk8PWeEj7ZXsg954ziTxePIzkikM93Wo/iyutbtdGgk96XwUeQGhVEXlUTu3X35nEp4XbjxoUFUFHfSqduops7IpaIIK1iRIf4M3NoNN/ojWp+VRM+wr5p5OLJyRh8BB9vK+ST7YW0d0qunm6/Ys8YGs3O/Frz/M2n2wsprm3p4i5v4tqZ6fj7+jAhJYKfzRtu9dslU1Ioqm3hr98e4KFPdnPqyFh+eeZIh89m+tBoIoL8aGrr5PKpqXYbvLBAP67MSuWLXUWU1R9XPD8cKGdYbIjZLBXga+Cf106hsbXTau7o1TVHCPE3sGCctVno3HEJbM+rsVJmJvfz8Xr53Dw3g8unpvKP5TlWi5rrW9r5dHshF01KdtgxAYgPD6SsvoWc0gaiQ/zNVgFbTDuaHyipZ0RCaJfnkB4TzMWTknlnYx4vrTyEBIflOX2o1tD+64dcfvLfjSRFBLJwclcFdXam1uG5462tvLTyMLGh/kQF22+E7zpzBNVN7by7MY+XVh0iJTLIymTYW8YlRzj1+gN4+IJM/nbFRKcmVdCey41zMpia7lzhAExMjeSDn8/mxeum2Z1H7CsGrYKKDQ3g81+ewuxhMby25miPtk/ZqZtSpqZH8ceLMnntxixSooJ47Iu93PFW1zVJbR1G1utrKiy9uKSU5kZvyeY8hsaGMGtYV5NAZnI4s4ZF88a6o3y5u4TJaZFmM5AJk8Kyt0VJp1Fy/wc7ufOdbfz5y738eMC+i/WXu4uJCPLjsYXjEEJw7/xR2h5dH+/qssOxlJJ//5jLsNgQ7jxjBD4+ggsnJbM6p9xqxf/GI9o8nTMFBZAeE0J+VRPZhbX4G3ysTDiWmEZQ6w5VUFjTzJVZ1iavBeMTyS1rILesnryqJpIjg+z2ouPDAjl1ZCyfbC/kvc15TEqLZHSifZlZQ6Jo6zSyM7+GTqPkhZWHGJ8Sbl59b0tsaACf3TmXt26Z0UX2OZmaa/iLKw8xLjmcF6+bZj411h5+Bh/OGhOPj9AWcDrihtkZdBglb68/BmimuA2HKzljTLxVvOFxoVw9PY0PtuRTUN3E0YpGPt9ZxHWzhlithQGYr4/SLc18ewpriQ31J1Gf+BdC8OdLxzMlPZJ73t9p3nrp0+2FNLZ1cv0s53MwCeEBtHdKNh2tcjh6Aq3zUd3URk5ZA6MdvBu/mDec5vZO3lx/jFNHxlk5x1gSHxZIRkww3+8vY3RiGJ/dOdfsgGNJbGgAH/5sNi9eN5WXr5/GkttnORwRTRsSzaxh0fxjeQ6bj1Zz66lD7b53fUlEsB9XZqV1O2q7ae5QHrl4nMv3HZMYbuWY0x8MWgWVFBGIv68Pt582jJK6Fr7Y5dgs1tLeyaHyBjYfreJAyfE5qx35NYT4a0NlIQRnjkngfX1F+Oqcii4L/LbnVdPY1snohDC+2VOi7enVYeSG/21mzMPfMPnR79h8tJqrpzt+uW6eO5Si2hb2Fddxns3oCTSTTGZSON/v66p8VuWU88HWArbnVfPm+mPc/PpmK2830JTYigPlzBsdZ3brDvb35fcXZHK4vLGLG/uqnAqyi+r42enDzWstLpyYRHuntDILbTxcRYi/gfHJ9kdEJtKjgzhW2cSeolrGJIU5rNxxoQFUNrSyeFMekcF+XSrO/Ezt2XybXdplDZQtl01Npbi2hYOlDVztxCwyPSOaAF8frn91E5e9sI4jFY38Yt4Ipw3B2KRws/eZJcH+viyans6YxDBeu3E6IS70Sh9YMIbXb5pBYoRjb7CM2BDmZybw2tqjVDS0sja3krYOI2faKCiAX5wxHIHg3z/m8tKqQ/gafMxrzSwZGR/K0NgQvrMoz92FtYxLjrDKe6CfgRevm0agnw93v6dtTPrWhmNMTI1gkgPTpQmTh9uRika7HnwmooL9Kappoaqxza4jBcDIhDCz6fvaGY7LE+Dn84ZzyylDWXL7LOKdeNlNSY9iwfgk5o9LZES8fbkm7jpjJA2tHUQG+zkcvSlco7/Og4oUQnwohNgvhNgnhJgthIgWQiwTQuTof6P0uEII8bx+ttMuIcRUi/vcoMfPEULcYBE+TQixW7/meeGGwXfe6DhGxofy8qrDXeaFOjqNvLHuKNP/vJyznl7JlS+u56J/rjF76ezMr2FCaoTVIjiAG+dkkBge2OUgs1U55Rh8BM9dM1l3uT7KHz7bw6qD5dw4J4OLJiZz+dRUrpnueL3EWWMTzJP9C+woKNBMEluOVZnXi5hYvDGP2FB/Vtx/Bjv/OJ9JqZH833s7rHYL2JFfTVVjG2eNtW3wExgRH8oLK6xX6//7x1ySIgK5xKJXPyElgvToYCtnjQ2HK8nKiDYrPUekRwdT29zOtmM1Zk8je8SGBWCUmqPIJZNTunj6JUYEMiU9km/2lJBf1exUQc3PTCAswJcgPwMXTXJsjokK8efjX8zhp7OHUNXYysTUCLNjRk94+MKxfP3rU+322u2RGBHIaaO636LrgQVjaG7v5LnlOfywv4zQAF+764+SIoJYNCOND7YU8OHWAq7KSrXbSGtOGgmsP1RJbXM7Le2d5JQ12HVgSQgP5MnLJrKnsI5b39zCwdIGrpvZvQdbQvjxZzDSiQKICvYzr0ka5USRPXT+GH52+vAu77EtV09P5+ELM7s1m7nD3BExXDY1hfvPHU2wf/+Zw05G+msE9RzwjZRyDDAJbceHB4HvpZQjge/1/0E712mk/rkdeAFACBEN/BGYiXbExh9NSk2Pc5vFdS5vRiuE4LbThrG/pN68VQ5onk8X/2stf1yazeS0SJ69ehLPXj2Jtk4jn2wrpKW9k73FdXZ7hoF+Bu46cwRbj1Wz4uDxTWlX51QwNT2SMYnhLBifyKurj7Bkcz53njGcRy4ex2OXjOfpqyZ1MbFYYvAR/P6CTG45Zah5At+Ws8fGY5RYrSovq2vh+/1lXD4tFX9fHwL9DLz802lEh/hzyxubzfugLd9XhsFHcLpNQ+jjI7hDf06mPG09VsWmI1XceuowK/OUEIKLJiWxNreCIxWNVDS0ktPN/JMJkyJpbu+02wCaiNMbdaOki3nPxIJxiewurKWiodWhmQe08nrgvDH8ZsHobid4xyVH8PsLM1n9wJksveuULp0TdzC5pHua4XGhXDMjjXc35fH1nmJOGRHr0Hz4i3kj8BECo4Q7ThtuNw5oI9IOo+S1NUfYX1JPp1Ga559sWTA+kauz0lh1sJzwQF+zJ6Az4sOOK0ZnJr4oC09JRyY+gCExITx43ph+N6+BVq7PXDWZn7igmBXO6Y/dzCOA09C3MpJStkkpa7A+98n2PKg3pcYGtE1lk4BzgWVSyiopZTWwDFig/xYupdyg79n3psW9XOLSKSmMTwnnd5/spqyuhUb9vJPCmmb+85OpvHnzDC6dksqlU1KZNiSKJZvz2FtcR3untLu4DeCqrDTSooN4+rsDdBolVY1t7C6sNXt73Tx3KB1GyfzMBO49Z7Q7yeXccYk8fKHjMxnHJ0eQEB5g5Un3wdYCOo3S7HwBWqPw6o1Z5jU4Ukp+2FfG9Iwos8OBJQsnp5AUEciLKw6xfG8pv3hnG9Eh/lxjx4xy3awhhAX68cvF21ijH4Q20868mi2WisRRAwjHtzsanxLucKR1ro2HozOunzWEG+d2NW+dqNx99iiC/AzUNLXbNe+ZSIwI5Lfnj+G++aOdPqMpaZHMGx3Hc9/ncNubWwDt6BJH/OGiTCalRvDzeSOcOkeYiLccQTlRPCZX/oggP6u98BQnJ/3RvRgKlAP/E0JsF0K8IoQIARKklCYbUAlgGou7e+5Tiv7dNrwLjo7b8DP48I+rp9Dc3sn9H+7igQ93kVNWz7+uncL5E5KserlXZ6VxqLyR1/TFgI5s6/6+Ptw3fzR7Cut4+LM9rM4pR0rMJpqsjGg+v+sU/nntFKsdjD2Bj4/grLEJrDpYTmtHJ0ajZMnmPGYPi+niHjsmMZzfLBjDjwfKeXbZQQ6U1nO2A7OIv68Pt546jI1Hqrj1zS1EBfvz5s0z7JoxkiKC+NsVmqnn4U/3EOxvcDoiMmEaQfn6CIcOEgCpUcH4CJxuH5MRG8IY3eHBmYnvZCQ2NIC7zhxBgK8P88Y4NwveOHcoP5/nePQE2jv1vxun88SlE2hu6yQuLMDujgYmQgJ8+fTOud3e10SAr4GoYD8ig/2IDe06Z2fCtGHsKDsefIqTj/4wkPoCU4FfSik3CiGe47g5DwAppRRC9PlZEVLKl9E2qiUrK8tK3oj4UH53/lge/kzbR+qh88Z0WdsCcMHEJP70eTZf7ComITygixedJQsnp3CgpJ7/rDjEN3tKiAz2s2qkJ3hwxbUtZ4+N592NeSzemEd5Qyv5Vc3cN9/+SO2G2Rl8m13C8z/kAji12y+ansb3+0qZMTSaX8wb4dTzbP64RG6am8H/1h7ltFFxLplbwgL9iA7RvMO620RzxX1ndFl8a8uFE5PILWsgI2ZwKSiAO04bxtVZaVZmsd4ghODamemcnRlPU2tntwrCXQWSFBFEaICv0+tMIyhnoyzFyUN/KKgCoEDf1Ry0nc0fBEqFEElSymLdTGeaMHF27tM8m/AVeniqnfhuc92sIewqqCXQz2A+UdKWkABfLp6czOJN+UzSDwtzxv3njqaupZ23N+RxwcSkXs1ZuMOc4bEE+Rl45PO9gOYi7WhC38dH8LcrJrHgH6tICA+0uwjRREiAL+/eNsvldDx43hjK61vNu1y4wpXTUrsswLRHugtK5/bTtIlye550JztCCI8pJ0viwwKhD/TDE5dNIMBJhwcgISwQP4NgUh927hTeg+iPY6+FEKuBW6WUB4QQj6CdqgtQKaV8SgjxIBAtpXxACHEB2vEZ56M5RDwvpZyhO0lsRRuNgXZ8xzQpZZUQYhPwK2Aj8BXwTynlV87SlJWVJbds2dKj/OzIr+GSf6/lNwvGuGTCMBolr687ymmjYrt1UfUkqw6WU93UxqxhMS5tVLn5aBW+Pl13KlYovIkjFY2kRwf3W2dP4T5CiK1Syqxe36efFNRk4BXAHzgM3IQ2//U+kA4cA67SlY0A/oXmidcE3CSl3KLf52bgt/pt/yyl/J8engW8DgQBX6OZE51mrDcKCrTtY6ZnRHt0Ww+FQqE4GTihFJQ3IoQoR1OMvSUW6HrGeP8xkPJV3gcOJV+VvTfLHyKl7H7RXjcMWgXlKYQQWzzRUzgR5au8D868D3b5gznv/S1/0G51pFAoFArvRikohUKhUHglSkH1npcHsXyVdyV/MMofzHnvV/lqDkqhUCgUXokaQSkUCoXCK1EKSqFQKBTeiZRyUH3QtlH6EdgLZAO/1sOj0XZIz9H/RunhY4D1QCtwn829FgAHgFzgQScyb9DvmwP8n618YCnaESR9Lf+ILmev/rcA2AHsBtqA6j6Q/Q1QA3xh8+wPAkf16z8BlruZ99fQtsfa0015m9J5FDhk8dw/1GVLtC2z+lJ2LvCknXJ/HmjoQbn3Vn6jRdkX6/fqC/nmeDZlv1//5OhhP7oqHwf1tz/KvoeyPVb2Hsr7IY7X+RaLNHhStt33AwdtrNN3qLsIJ9sHSAKm6t/D0BrKTOCv6A0t2l6Bf9G/xwPTgT/bFJhBL+xhaDtk7AQy7ciLRts9IxqIQlscfLqF/CK0xru8H+SPQWuYouzkvQDtWBSPydbjngVcpOfR8tl/jNY4ZuoV5hNX867/dhratlcOG0mbdKZb5DdML4ezgVrg0T6W7Y9WqS+3KPdjaB2TNnfK3YPyTc/iAPCep+XbxrMp+2fR3vdMYCWwwo333m797Y+y76Fsj5W9h/JuWed3Az/1pGxn7wcO2lhnn0Fn4pNSFkspt+nf69FGEik4OJ9KSlkmpdwMtNvcagaQK6U8LKVsA5bo97DF9hyrbwDTCW4SCEAbQYT3g/z9wJfAApu8XwEIYLWHZSOl/B6o178XSym36dtZnQZs1uVHAZFu5B0p5Sqgyp5MB+nMA/4HLNTzvkPPczDwXh/LbgPeBkbpvzXpct9B27DZnXLvtXyLsh8JjAB+3wfyreJZ1ju0fTa3cvy4HNNmz93Kd1J/neXfI2XfQ9keK3sP5d10zaVoo6JPPSzb2ftht411xqBTUJYIITKAKWibzDo6n8oRjs6ncifec4BRl+/bn/Jt8p4ELJZa18aTsh0Rg2ZamKzLjwRM26K4It9VXMm7ATCd7NjnsvXvf0BrnL5G86R1p9x7Ld8i/wlAp5Qypw/kOyMJrRe/EW3bnNieyLcpR1v6tOx7Ilv/7pGy90Dek4HlUso6D8t2hrtt7OBVUEKIUOAj4G69kMzoDbXsY/mzgcuBO9Aa6/6U74913v2Bxf0kG7Td7BMZmGfvx8CW+wjgXuC2AZJvfu/RetHmnnI/yg/G+vlLd+U7q79O8EjZ91C2x8reQ3nvUZ3vad5tcVXmoFRQQghTYb0jpfxYDy7Vz6XC5nwqR9g9t0oIMVMIsUP/XOwgXgnwKpqZ4RlgDeAjhFjXD/LTgatNeRdCTAI60ebCPC27C/qzfxnoQLPDg+ZEUe6GfLsIIdIs5P/MTjrTgTOwLvdO9J5cH8tORZtzew9t1Px3IcRR/doj/Sj/IzTz0io0U1Chm++9q/LtxTHVuyq0iXjQNh2tdEe+vfrbX2XfQ9keK3tP5F0IEYvW9m/tA9nOcLeN7ZcDC70Kff7jVWCflPIZi5+Wonm7PaX//aybW20GRgohhqK9EIuAa6WU2WimK5O8aOAJIYTpkKX5QATwnZTybj1Ohn4/U4Pdl/IXAW9b5P0a/V4ez7stls8eqEOb+1qC5j1Y60be7SKlzMc677426bwX+MKm3JvQFPYf+lj2IjSPqtVSymkW8dqAl/R/+0P+PinlM3pj8gVaI+FO2bsk3xabst9lIdPycNFu5Tuqv/1R9r2Q7ZGy92Der0Arh0W4WO6uyu4Gd9vYQenFdwra0HIX2mTpDrRJ2xjgezQXyOVoByiCZooqQGtQa/Tv4fpv56N5sxwCfudE5s1o7qa5wBN25N+I9sL0tfwCO7KLgVl9KHs12uioGa0xNMk3uTsXob2oP7gpf7Ge9nY9/BYH8k3pLLTJewFa770DzaW2tg9lH0IbNdp77xp68Ow9Ib8BeJievfeuyreMZ1n2u9EcZwrRvPhWuiofB/W3P8q+h7I9VvYeyvsO/fuVfSjb7vuBg3fN2UdtdaRQKBQKr2RQzkEpFAqFwvtRCkqhUCgUXolSUAqFQqHwSpSCUigUCoVXohSUQqFQKLwSpaAUCoVC4ZUoBaVQKBQKr0QpKIVCoVB4JUpBKRQKhcIrUQpKoVAoFF6JUlAKhUKh8EqUglIoFAqFV6IUlEKhUCi8kkF3HpSJ2NhYmZGRMdDJUCgUipOOrVu3Vkgp43p7n0GroDIyMtiyZctAJ0OhUChOONo6jJTUtlBY06x9qpsprGmisKaZopoW2HrGMU/IGbQKSqFQKBRdkVJS09SuKxv9U9uiKyHt//KGVmyPEowLCyAlMojM5HBWeCgtSkEpFArFIMJy9GNSQKaRUJE+Ampu77S6JsDXh5TIIJIjg5g3Oo6UyGCSIwPNYUmRgQT4Gszx//MTz6RVKSiFQqE4Sejp6Cc2NICUyEBGJYRxxuh4knXFoymgQKJD/BFC9Ht+lIJSKBSKE4S+GP0kRgQS6GdwIHFgUQpKoVAovACjUVLR2EpxTQvFtS0U1zZTfAKPfjxBtwpKCHGZs9+llB93c/1o4D2LoGHAH4BI4DagXA//rZTyK/2ah4BbgE7gV1LKb/XwBcBzgAF4RUr5lB4+FFgCxABbgeullG3d5U2hUCj6AykllY1tlNS2UFSjKZ6i2mZKalsortG+l9a10N5prX1O5NGPJ3BlBHWR/jcemAP8oP9/BrAOcKqgpJQHgMkAQggDUAh8AtwEPCul/LtlfCFEJrAIGAckA8uFEKP0n/8NnAMUAJuFEEullHuBv+j3WiKEeBFNub3gQt4UCoWiV0gpqW5q10Y8NcdHPsW6Miqp0763dRitrvMzCBIjAkmKCCJrSBRJkUEk6f9rf0/s0Y8n6FZBSSlvAhBCfAdkSimL9f+TgNfdlHcWcEhKeczJQ18ILJFStgJHhBC5wAz9t1wp5WFd/hJgoRBiH3AmcK0e5w3gEZSCUigUvURKSW1zu9nkVlTToo2CdGWkKZ9mWtqtlY+vjyAhPJDkyEAmpUayYJymcCyVUEyIPz4+g1f5uII7c1BpJuWkUwqkuylvEbDY4v+7hBA/BbYA90opq4EUYINFnAI9DCDfJnwmmlmvRkrZYSe+FUKI24HbAdLT3U26QqE4mZBSUtfSYa1wajWvN/MoyI7TgcFHkBAWQJK+5ufssfHHRz2RQSRHBBIbGqCUjwdwR0F9L4T4luMK5mpguasXCyH8gYuBh/SgF4DHAKn/fRq42Y30uI2U8mXgZYCsrCzZTXSFQnGCYjnyKaltMZvZSizMb8U1zTS2WSsfHwHxYYEkRgQyJlFzOjCb3SIDSY4IIi4sAINSPv2CywpKSnmX7jBxqh70spTyEzdknQdsk1KW6vcrNf0ghPgv8IX+byGQZnFdqh6Gg/BKIFII4auPoizjKxSKk4xOo6SiofW48qltpqSu1ax8SnVl1Goz5yOE5vGWHBHIiLhQTh0Za1Y+yZGBJEYEER8WgJ9B7aHtLbjlZq577Dl1inDCNViY94QQSRYmw0uBPfr3pcC7Qohn0JwkRgKbAAGM1D32CtHMhddKKaUQ4kfgCjRPvhuAz3qYRoVCMYC0tHdSVtdqntsxjX4s/5bVt9JptDaA+Bm0OZ+kiEDGp0RwTmYCiRFBJIZro6HEiEClfE5AXFZQ+ujpL2jefEL/SClluAvXhqB5391hEfxXIcRkNBPfUdNvUspsIcT7wF6gA7hTStmp3+cu4Fs0N/PXpJTZ+r1+AywRQjwObAdedTVfCoWib5FSUtfcQWVjK1WNbVQ2tlHV2EZFfSvFJuWjK6Cqxq6rQ0L8DWZvtznDtVFPQkQgSRbKJzpYORycjAhpu+rLUUTNm+4iKeW+vk1S/5CVlSXVbuYKhWtIKWls66SuuZ36lg7qW9qpa9G+1zW3U9fSoX1vaaeuuZ0qXQlVNrZR3dhGh9F+OxMT4m8e+ZiUToLuYp0UEUhCeCBhgX79nFtFbxFCbJVSZvX2Pu6Y+EpPFuWkUAwmpJS0dhitFEq9hUKptwmvM4friqi5nYbWDhzoGDP+Bh/CAn0JD/IjOsSftOhgJqdFEh3iT3SIPzGh/kSHBBCj/x8d4n9SLzJV9B53FNQWIcR7wKdAqymwFztJvKmHZ6CZ+K6SUlYLbYHUc8D5QBNwo5Rym36vG4Df6/d5XEr5hh4+DW1NVhDwFfBr6erQUKHwcjo6jRbKxDRK6TiuWKyUSQf1rV0VUVun0akMHwGhAZpyCQv0IzzQl5TIIMIDw/QwX035BGq/mxSRZbhSNgpP446CCkdTGPMtwiQ930niQeB7KeVTQogH9f9/g+btN1L/zERzR58phIgG/ghk6XK36jtJVOtxbgM2oimoBcDXbuRNoegTjEZJY1uHbgKzVRztVqMV23BT/CYbV2h7hPgbrBRHdIg/Q2JCCA/0tQoPd6BoQvwNg3rHAoV34o6b+U0ekGe5k8RCYJ4e/gawAk1BLQTe1EdAG4QQkfquFfOAZVLKKgAhxDJggRBiBRAupdygh78JXIJSUAoP0K6PXkyjE9Mci+Uops48D3M8jmlk09Da0WVzT1v8fX0INysNTaEkRgQSFmA7UvGzUjgRenhogC++yjtNcRLijhffKLSRSoKUcrwQYiJwsZTycTfkWe4kkWDhZl4CJOjfU+i6Y0RKN+EFdsLt5UHtJDHI6Og0UtPcbp7I76pcrBWPpQmtrqW929GLEBAWYBqdaAojLTrYPEoxKZTwIIuRjIUiCgv0VaYxhcIB7pj4/gvcD7wEIKXcJYR4F3BJQdnZScKMvpapz+eM1E4SJwcNrR2U1WnrYSob2qhstP3bRmVDK5WNbdQ0tTu9l8FHaKMXXcGEB/kyLDSU8CBdwehmMcu5mfCg4+Eh/r7KvVmh6CPcUVDBUspNNnbqDkeR7WC1kwRQalqsq5vwyvRwRztJFHLcJGgKX6GHp9qJrziBkFJqiqe+ldK6Fsr1v2V1rZTWt5oVUlldS5ftaUxEBfsRExpAdIg/oxPDiAkJMHuLRQT52SgdbfQSrOZeFAqvxR0FVSGEGI7moIAQ4gqg2PklVljtJIG2Y8QNwFNY7/6wFG0T2SVoThK1uhL7FnhCCBGlx5sPPCSlrBJC1AkhZqE5SfwU+Kcb6VL0IVJK6k0jnrpWSuv1v3WtlOnfy+o15WPPnBbo50NCuLYLQGZyOPNGx5n/jw8LJDbMn5iQAKKC/dQ8jEJxkuGOgroTzTw2RghRCBwBfuLKhQ52kngKeF8IcQtwDLhKD/8KzcU8F81r8CYAXRE9BmzW4z1qcpgAfsFxN/OvUQ4SfY5pd4Cy+hazsim1UDamEU9pXUuXowgAgvwMJIQHEB+ubU1jUjpm5aP/Fhbgq0Y4CsUgxaWdJHT38L9IKe/TlY2PlLK+z1PXh6idJBzT3NZJSZ226WapxT5opTajINvNOEFzd04IDyTOQtkkhAdqCifM9DeAUKV4FIqTln7bScK0S7gQ4hQAKWVjb4UqBgajUTt22lbp2H6va+k6tRjibyAhIpCEsECmpkdZjHRM5jbte2iAW/sPKxQKhUNcaU02AVOB7UKIpcAHgFlJdbeTBIAQIhJ4BRiPNod1M3Au2uLacj3ab6WUX+nxH0I7tr0T+JWU8ls9fAHaLhMG4BUp5VN6+FC0ncxjgK3A9VLKrrtOnsS0tHc6VTom81t7p/WI2Uc/giAxIpAhMSHMHBpDor4HmrYTdIDaD02hUAwI7nR3A9HOXjoTTckIXNhJQuc54Bsp5RW6u3kwmoJ6Vkr5d8uIQohMtPVS49CO21iur8EC+DfaXFYBsFnfSWIv2i7rz0oplwghXkRTbifFke9Go6SqqU1XMrrSMR09UNdq/l7b3NWdOtjfQGK4pmxmDI3WlU7AcQUUEUhcaIByLlAoFF6JKwoqXghxD9p5TSbFZKLbCSwhRARwGnAjgD6yaXMy/7AQWCKlbAWO6Luoz9B/y5VSHtbvuwRYKITYh6Y0r9XjvAE8wgmgoDo6jZTVt+onfGpn31ieAKqdfdN11GM6eC0xPJC06GCmD40yK6LECG3kkxChHAwUCsWJjSsKygCEYq2YTLiy2HUomhnvf0KISWgmuF/rv90lhPgpsAW4V99XLwXYYHG95c4QtjtJzEQz69Xop+naxreiP3eSsKd8impaKKlr1v7qysd2h+ggP4M+wglgekYUCbrCMSmdRN0BQR28plAoTnZcUVDFUspHeyljKvBLKeVGIcRzaBvD/gt4DE3JPQY8jTY31Wd4aieJ3iifpEjtnJtTLI6bTooI1MLDgwgPUqMehUKhANcUVG9bywKgQEq5Uf//Q+BBix0lEEL8F/hC/9fRThI4CK8EIk3ehvRyJwmlfBQKhcI7cEVBndUbAVLKEiFEvhBitH70xlnAXtM2R3q0S9HmuEDbSeJdIcQzaE4SI9E8CQUwUvfYK0RzpLhW38fvR+AKNE8+y10pHFLb3M4rqw8r5aNQKBReSrcKymK3ht7wS+Ad3YPvMNruEM8LISajmfiOou8yIaXMFkK8D+xF2+vvTillJ4AQ4i7gW7R5sdeklNn6/X8DLBFCPA5sB17tLkF5VU08/uU+pXwUCoXCS3FpJ4mTkfGTpsi16zcp5aNQKBQexlM7SQxaBSWEKEfbA7C3xAIVHrjPiShf5X3gUPJV2Xuz/CFSyrjeChq0CspTCCG2eKKncCLKV3kfnHkf7PIHc977W75aTKNQKBQKr0QpKIVCoVB4JUpB9Z6XB7F8lXclfzDKH8x571f5ag5KoVAoFF6JGkEpFAqFwitRCkqhUCgU3omUclB90Pbz+xFtp4ps4Nd6eDSwDMjR/0bp4WOA9UArcJ/NvRYAB4BctP0FHcm8Qb9vDvB/tvLRtnfa1w/yj+hy9up/C4AdwG6gDajuA9nfADVoey1aPvuDaDuI5AKfAMvdzPtrQBmwp5vyNqXzKHDI4rl/qMuWwIo+lp0LPGmn3J8HGnpQ7r2V32hR9sX6vfpCvjmeTdnv1z85etiPrsrHQf3tj7LvoWyPlb2H8n6I43W+xSINnpRt9/3AQRvr9B3qLsLJ9gGSgKn69zC0hjIT+Ct6Q4u22/pf9O/xwHTgzzYFZtALexjgD+wEMu3Ii0bb3ikaiEJbHHy6hfwitMa7vB/kj0FrmKLs5L0A7dwuj8nW454FXKTn0fLZf4zWOGbqFeYTV/Ou/3Ya2i75DhtJm3SmW+Q3TC+Hs4Fa4NE+lu2PVqkvtyj3Y2gdkzZ3yt2D8k3P4gDwnqfl28azKftn0d73TGAlsMKN995u/e2Psu+hbI+VvYfyblnndwM/9aRsZ+8HDtpYZ59BZ+KTUhZLKbfp3+vRRhIpaAclvqFHewO4RI9TJqXcDNgeWTsD/QBFqR3CuES/hy3nAsuklFVSO+/qG7RNcEHrwQWgjSDC+0H+fuBLYIFN3q9A24x3tYdlI6X8HqjXvxdLKbcJbW+p04DNuvwoINKNvCOlXAV0t0+kZTrzgP8BC/W879DzHAy818ey24C3AdPJ0E263HfQ9sN0p9x7Ld+i7EcCI4Df94F8q3iW9Q44H+1cuBT9k+qqfCf111n+PVL2PZTtsbL3UN5N11yKNir61MOynb0fdttYZww6BWWJECIDmAJsBBLk8d3VS4CEbi5PoesBivYKzFm85wCjLt+3P+Xb5D0JWCy1ro0nZTsiBs20MFmXHwmYtkVxRb6ruJJ3A2A6+qXPZevf/4DWOH2N5knrTrn3Wr5F/hOATillTh/Id0YSWi9+I9q2ObE9kW9Tjrb0adn3RLb+3SNl74G8JwPLpZR1HpbtDHfb2MGroIQQocBHwN16IZnRG2rZx/JnA5ej7eLe0M/y/bHOuz+wuJ9kA4QAiQzMs/djYMt9BHAvcNsAyTe/92i9aHNPuR/lB2P9/KW78p3VXyd4pOx7KNtjZe+hvPeozvc077a4KnNQKighhKmw3pFSfqwHlwohkvTfk9Am+Zxh92BFIcRMIcQO/XOxg3glaEeCCOAZYA3gI4RY1w/y04GrTXkXQkwCOtHmwjwtuwv6s38Z7SiVpXpwDdqchKvy7SKESLOQ/zM76UwHzsC63DvRe3J9LDsVbc7tPbRR89+FEEf1a4/0o/yP0MxLq9BMQYVuvveuyrcXx1TvqtAm4kHbdLTSHfn26m9/lX0PZXus7D2RdyFELFrbv7UPZDvD3TbWpQMLTyr0+Y9XgX1SymcsflqK5u32FK4dergZ+wcoZqOZrkzyooEnhBBRetB8IAL4Tkp5tx4nQ7+fqcHuS/mLgLct8n6Nfi+P590Wy2cP1HH8kMlqtAlrV/NuFyllPtZ597VJ573AFzbl3oSmsP/Qx7IXoXlUrZZSTrOI1wa8pP/bH/L3SSmf0RuTL9AaCXfK3iX5ttiU/S4LmZanX3cr31H97Y+y74Vsj5S9B/N+BVo5LMLFcndVdje428YOSi++U9CGlrvQJkt3oE3axgDfo7lALgei9fiJaDbcOrSefgEQrv92Ppo3yyHgd05k3ozmbpoLPGFH/o1oL0xfyy+wI7sYmNWHslejjY6a0RpDk3yTu3MR2ov6g5vyF+tpb9fDb3Eg35TOQpu8F6D13jvQXGpr+1D2IbRRo733rqEHz94T8huAh+nZe++qfMt4lmW/G81xphDNi2+lq/JxUH/7o+x7KNtjZe+hvO/Qv1/Zh7Ltvh84eNecfdRWRwqFQqHwSgblHJRCoVAovB+loBQKhULhlSgFpVAoFAqvRCkohUKhUHglSkEpFAqFwitRCkqhUCgUXolSUAqFQqHwSpSCUigUCoVXohSUQqFQKLwSpaAUCoVC4ZUoBaVQKBQKr6TPFZS+HfuPQoi9QohsIcSv9fBHhBCFFlu1n29xzUNCiFwhxAEhxLkW4Qv0sFwhxIMW4UOFEBv18PeEEP59nS+FQqFQ9C19vlmsfu5HktSO+g5DO4PkEuAqoEFK+Xeb+Jlou+HOQD/1kePHJR8EzkHbIXczcI2Ucq8Q4n3gYynlEiHEi8BOKeULfZoxhUKhUPQpfX4elNSO+C3Wv9cLIRyeZa+zEFgipWwFjgghctGUFUCulPIwgBBiCbBQv9+ZwLV6nDeARwCnCio2NlZmZGT0KE8KhUKhcMzWrVsrpJRxvb1Pvx5YKKzPsp8L3CWE+CmwBbhXSlmNprw2WFxWwHGFlm8TPhPtjJEaKWWHnfi28m8HbgdIT09ny5YtHsiVoj/58UAZqw9W8IeLMgc6KYp+5J2Nx3h19RG+v/d0tLPzFN6MEOKYJ+7Tb04SoutZ9i8Aw9FOYywGnu7rNEgpX5ZSZkkps+Lieq3cFQPA+5vzeX3dETqN6hyzwcSGw1Ucrmikub1zoJOi6EfcHkEJIVKAIZbXSilXdXNNl7PspZSlFr//F+34adBOgEyzuDyV48dC2wuvBCKFEL76KMoyvuIkY19xHUYJ1U1txIYGDHRyFP3EobIGAKoa2wj271fDj2IAcaukhRB/Aa5GO67b1JWRgEMF5egseyFEkj4/BXApsEf/vhR4VwjxDJqTxEhgEyCAkUKIoWgKaBFwrZRSCiF+BK4AluDqWfeKE47G1g6OVTUBUNHQqhTUIMFolBypaASgpqmd1KgBTpCi33C3K3IJMFp3YHCVucD1wG4hxA497LfANUKIyWgK7ihwB4CUMlv3ytsLdAB3Sik7AYQQdwHfAgbgNSlltn6/3wBLhBCPA9vRFKLiJGN/ST0mp9OK+jZIHNj0KPqH4roWs2mvuqltgFOj6E/cVVCHAT/AZQUlpVyDNvqx5Ssn1/wZ+LOd8K/sXad79s2wDVecXOwtrjN/r2hwp4+kOJE5XN5g/l7d1D6AKfEeXlx5iH3FdTy3aMpAJ6VPcddJognYIYR4SQjxvOnTFwlTKGzZV1xHgK/2yioFdXIgpSS7qBajE6cX0/wTQI0aQQHw4/4yvtpdTFuHcaCT0qe4q6CWAo8B69AW3Jo+CkWPkFLi6mLxfcV1TEqLxN/gQ3m9UlAnAx9vK+SC59ew7lClwziHKxoJ8TcAUN2oRlAABdXNtHdKcsrqBzopfYpbCkpK+QbaLg8mxfSuHqZQ9Ii739vB6X9bwaYjVU7jGY2SAyX1ZCaFExvqT7kaQZ3w1Da188RX+wDIr25yGO9weSMj4kMJC/BVc1BAR6eRkroWAPYW1XUT+8TGLQUlhJgH5AD/Bv4DHBRCnOb5ZCkGA2tzK/hsRxGVDa1c/fJ6nvp6v8PR1LGqJpraOhmbFEZsWAAVDaqhAnj6uwPc+/7OgU5Gj/jbd/vNCqesznGH41B5A8PjQokM8VMmPqC4tsW8DjBbKSgrngbmSylPl1KeBpwLPOv5ZClOdjo6jTz6+V7SooNY/ZszuWxKKi+uPMSGw/ZHUvt0B4nMpAhiQwOoUCY+pJR8sKWAz3YU0tTW0f0FXsTO/Bre2ZjHDXMyiA7xp6y+xW68xtYOimtbGBYXQlSwv9c4SUgp+WJXEQ2t/f/cTaNNP4Owchw6GXFXQflJKQ+Y/pFSHkTz6lMo3GLxpjwOlNbzu/PHEh3izz3ztf2ATetdbNlXXIfBRzAyIZTYUH/lJAEcq2yipK6FDqPs1kTqTazNreCm1zcTFxrAPeeMIj4sgDIHHQ7T+zA8LpTIYH+vGUFlF9Vx17vb+dXi7U4dPPqCgqpmAOYMj2VfUV2/y+9P3FVQW4QQrwgh5umfV9D20VMMIFJKth6rIvcEmTCtb2nn6WUHmT0shnPHaYuZEsMD8TMIh3MR+4rrGBYbQqCfgdjQACob2+xWzOLa5j5Nuzex4bDmWCAErHfiZOBNvLL6MNe/upGYEH+W3D6LsEA/4sICKKuzP4I6pLuYD4sLJSrYz2tGUKZ0/bC/jGeXH+xX2QXVTfgIOHtsPPWtHU7n70503FVQP0dbQPsr/ZOth52QZBfVml+0ExEpJW+uP8pZT6/k8hfWc+P/Np8QvalPtxdS09TO/QtGmzf+NPgIkiODyK/qWtk6Oo1kF9UxNikcgNjQADqNkppm68ZqxYEyZj/5A9vzqvs+E17AhsOVxIUFMD0jmrWHKgY6Od1SWNPM41/u48wxCXx651yGxYUCEB8W6HAEdai8ER8BQ2KCdROfd4ygjlQ0IgRcOiWFf/6Qyzd7SvpNdkF1M4nhgUxKiwQ86yjxw/5Sth7znvrjrhdfq5TyGSnlZcCtwPdu7irhNewtquOC59dw1Yvrae/0vrUErmyGui2vhj98lk1YkB8/mZlOQXUz6w97d09aSslbG44xISWCKXoFM5EWFUx+tfUIqNMoufeDnRTXtnDW2HgAYsO0LY5szXxf7S62+uuI2uZ2hz32EwUpJRsOVzFrWAxzh8eSXVTnNeYvR2zQR3n3nTuKkIDjewTEhwdQXt9qt3N1uLyB1KhgAv0MRAb7Ud/SQYcX1NejFY0kRwTx1OUTGJcczlNf73N5uURvya9uIjU6mFEJYRh8hMccJYxGyT3v7+SWNzZ7Tf1w14tvhRAiXAgRjeZm/l8hxAnpJBEe5Mftpw2jsrHN6RqMgUBKyZUvruOalzdQ3+LYpLEmpwIh4PUbp/PwhZmEB/ry/pZ8h/G9gY1HqjhY2sD1s4d0OTYhLTqIAosRVKdRct8HO/lsRxH3nzuahZO1U1RiQ7UDky0dJTqNku/3lQGwbG+p3cZi67Eq7np3G9P/vJyznllJUU3vzYG1ze38esl2/rMi12rHg77mqD7/NGtYNHNGxCAlDh1MesJDH+/i9bVHPHY/gI1HKokM9mNUfJhVeHxYAB1G2WV01NjawYGSeobHhQAQFayVu+3IuSe4okyklLR22N89/UhFI0NjQwjwNXDNjHSOVjZxsLT35X+sspHnlufw44EyWhzs3F5Q3UxqVBCBfgZGxIW65ShR1djGI0uzKbBjFtxXUkdNUzs1Te385qNd/aZwneGuiS9CPyrjMuBNKeVM4CzPJ6vvSY0K4t75owgL8OWLnUU9vs97m/P4w2d7XDat1Ta3s7/E+Qu19Vg12/JqWH+4kutf3URpXQsvrDhE1uPLeGfj8WNW1h6qYFxyOFEh/gT6GbhkSgpf7ymh1oN2+rYOo0df1Lc2HCMiyI+LJiZ3+S01KpjKxjYadc+oT7YX8sn2Qu49ZxR3njHCHC9O3yTWci3UjvxqKhvbmDE0mqOVTV1Mtw2tHVz7342sya3gimmpdBqly5WwvdPocGHwJ9sK+GxHEX/95gBnPr2S336yu/uH4IT/rjrMne9s4+FP9/Cnz7O55uUNTHtsGW+sO2oVzzT/NGtYDJNSIwn2N7DOQ2a+3LJ6Fm/K54mv9nPUgdNKT9hwuIoZGdH4+Fh3TOLDAgHMZr5jlY1c9dJ6Jv3pO3LKGhiXHAFAZLDmj2U7Uvxxfxk3vLaJwm46HPtL6nhkaTYX/nM1ox/+hlUHyx3GPVBSz9Uvb2D648u7yJNSclhXUADzMxMA+C7buZnv2+wSp52itbkVXPyvtTy7/CA3/W8zUx9bxkdbC6zitHVoa6BSo4IByEwOJ7uo1qlcE8W1zVz10npeX3eUD23uC8fnMe84fRg/HihnyWbHnd1Oo2RPYS2vrTnCigNlLsnvCe4qKF/9CPerOH48hlcghFgghDgghMgVQjzoyjUBvgbOGZfAt9klPd4y5LU1R3lz/TH+8X2OS/H/8s1+Lv7nWqdD6MWb8gkN8OW5RZPZW1TH7Ce/5y/f7KeprZM31h1FSklTWwfb86qZOzzWfN1VWWm0dRhZutMzp40U1zZz+t9+5A+fZXcf2QXK6lr4dk8JV05LJUjfGcCStGit0hXoZr6tx6oJD/TlrjNHWMUz7WJuuRZq2d4yfH0Ej18yHoDv9pZaXbO/uI7WDiN/v2IST1w6gYfOG8PqnAqnlRCgpLaFK19cz+l/+9GuCe2jbYWMSw5n7YNncsW0VN7dmNfjOYENhyv581f72HKsii92FbFkUz5NbR2EBPjy0spDVmZf0/zTsNgQ/H19mJ4R7TFLwAdbCzD4CPwMgj99nt2tEpdS0tLe6TReUU0zeVVNzBoW0+W3hHCtPE0K6rvsUjYdqeKWU4by9i0zufvskcDxEZTJUaK5rZOHP93DTa9vZuXBcl5Zfdiu7E6j5D8rcrnon2t4b3M+YQF++Aj4fl+p3fj//jGX859fze6CWupaOro818rGNupbOswKKj48kCnpkXy717GC2lNYyx1vbeXyF9Z1UfodnUZeXnWIn762iYTwAJb932m8ftN0RsaH8uev9pk7bKbnKCWkRQUBMC45nNK61m69Wo9WNHLFC+spqW0hNtSf3QVdldqGw1VkxATzm3PHMHdEDI99sdduZ7e6sY2ZT3zPhf9cw6Nf7OVXi7f32TIHdxXUo2i7iedKKTcLIYahLdwdUIQQBrTFw+cBmWg7pbt05OpFE5Opa+lgdY7j3pSJHw+U8eLKQ+b/KxtaOVBaT2yoP89/n8M3e7Qe0vub8+1O1BuNkmV7S2nrNPLG+qN2ZdQ2t/Pl7iIumpTMwskpvHJDFgvGJ/L+HbN58LwxHCxtYF9xPZuPVtPeKZkz4riCGp8SQWZSOO9v6do7cpeW9k5+9tZWimtbeHvjMfM6JGd0GiWHyhuodWCC+XBbAR1GyU9mDbH7u6nSmRwl9hbVkpkc3sUUGBHkh6+PsKqUy/aWMGtYDKMSwpiQEsFyGwVlMoNkJmuOFj+ZOYS5I2J4/Iu9DnveW49Vc9G/1pBdVEtTWyerc6xHKAdL69ldWMtlU1NJiQzi4QsyCQv05R898Opqae/koY93kx4dzIr7zmD7H+az77EFfHbXKTx43hiKaltYeVDrqWrzT5XMGhZjfjZzR8SQW9ZAaS/nDjo6jXyyrZAzRsfzf+eM4scD5SzfZ7+HXFLbwrTHljH0oa8Y8/A3/Oxtx7uebTyiNfIzh0V3+c08gtLTnlNWT0yIPw+dP5ZTRsbia9CaKbOCatQ6Co9+kc1bG45x6ylDuWBCEh9uKbBqzEF7Vje/vpm/fnOAczITWPfgmSy+fRZT06PYaqeOltW18Myyg5wxOp5VD5xBaIAva3Kty92kYIbqpkeAc8clsqewzuG79OHWAvwNPrR2GLnqpfVsz6smr7KJNTkVXPjPNTzx1X7OGhPPx7+Yy8iEMOaNjuePF4+jqrGNtzYct5qYOm+mEZRpdLnigOP2q6C6iWv/u4Gmtg4W3zaLU0fGsbvQWkF1GiUbj2jvlI+P4J5zRtHU1tkl7wBbjlVT0dDKQ+eN4d/XTqWupYPPdvTcCuUMd50kPpBSTpRS/kL//7CU8vI+SZl7zEBTmoellG1o50ItdOXCuSNiiQjy44tdjifWWzs6+dPn2dz0v8089fV+szu3ae3J89dMYVJaJHe+u405T/3AAx/t4rpXNnbpSe8qrKW8vpXIYD/e2Zhnt9exdGcRLe1Grpmhnc142qg4/vOTacwYGs0FE5Iw+Ag+21HIutwK/A0+TM+wPhznqqxUdhfWcqCk5y7nUkp+98kedhbU8rcrJhIW4MtTX+93GL+qsY2fvLKBcX/8hrOeXsnPHTRU6w9VMiYxzNzztMVU6fKrm+joNLK/pJ7xegW0xMdHEBPqb56DOlzewKHyRs7WnSjOyUxge36NlVlub1EdkcF+JEUEmu/x1GUTaW7v5L1NeV1k7C6o5bpXNhLsb2DpXacQGezXpRH4eFshBh/BxZM0c2VEsB+3njKM7/aWdumhNrV18PraI9z57jbOfHoFf/1mv9Ucwz9/yOFIRSNPXDqhy+jynMwEYkMDeHejls5vs0sorWtlzvDjo5F5o7W8O3uP86uaeGRpNne8tYV1uRV2Rzyrcyooq2/limmp3DAng5Hxofzp82y7jkQrDpRR2djGbacO5dxxCXybXerQK3bj4SrCA30Zkxje5bd4mxFUblkDw+NDu8Q7buLTOkDbjtVw1ph4fn9hJrecOpT61g4+3mbdOTtc0cjKg+X86qyR/PvaqUSFaEoua0gU+4rruyi097fk02mU/O6CscSFBTBrWAxrbDomh00KKsZaQYF9M19rRyef7ihk/rgEFt82C6OUXPqfdZz2tx+57tWN1Ld08OJ1U3np+mmEWjiPTE2P4rRRcby86rC5rTDNHaVFa525GUOjGZ8Szl+/2W930XBZXQs/eWUjDa0dvH3rTCakRjA+JYKy+lYrK87eojrqWzqYrb9Tk1IjCQ/0NXeKLNmZX4PBR/DT2RmcPyGRsUnhZsuOp3HXSSJQCHGnEOI/QojXTB+Pp8p9UgBLW02BHmaFEOJ2IcQWIcSW8nKtsfH39WHBuESW7S21ajD2l9Rx1UvrOeeZlcx58gf+t/YoV2WlAphdSjccriTIz8D0jGheum4aCycn87vzx/Le7bMID/Lj5tc3U1J7/CVYvrcUg4/g6SsnUdPU3sW+DLBkUx6ZSeFMSOnaMMeEBnD6qDiW7ixidU4FU9Iju5wuev6EJE2WA/PF0p1FvL8lnw2HKx06YPywv4yPthXw67NGcmVWGnedOYKVB8u7VFQT72/JZ21uJYump3PJ5GTWHars4jDQaZRsz6th2hDHp83FhvoT5Gcgv6qZQ+WNtHYYGZfStUHT4gaYR1CmvJ6tzwWcPTYBKa1NOHuL6xhnMxpLiw5m1rAYPt9VbFW5CmuaufmNzUSH+PPBz2YzNimcU0fGsfJguXmusdMo+XR7IfNGxREXdvzgxJtPySAiyK/L2pjHvtjHI5/vZUdeDQlhgfxnxSEW/GMVz3x3gJv+t4kXVx7mimmpnDIyFlv8DD5clZXKD/vL2Hqsmt98tJsJKRFcPjXVHGdUQhiT0yJZsimvS0MhpeThT/cw7+8reGfjMbYcrebaVzZy2QvrrN5PgA+25hMd4s+ZY+LxM/hwzzmjKKhuNs95WbLukGZm/O35Y3n8kgn4GQRvrT/WJR5odWXG0BgMPl1P3gn0MxAW6EtZXQtSSnLLGhhpR0GZlEt1UxsdnUYOVzQwMkFzuJiSFsnE1Ahet2koTe/s5VNTrMp+6pAoOo2SHfk15jCjUbJ4Uz5zhseYO1Gnjowlr6qJvMrjTgVHKhrx9RGk6iN+gKGxIYyMD+W77K71bvneMmqa2rkyK43RiWEsvesU/nrFRP5+5ST+de0Ult1zGgvGJ3WxFAD8+qyR2ihKf6751U0YfASJ4VpHy+AjeHTheMrqW3neZppBSslNr2+mor6V12+eYR5tmdoWy1GU5ZwmgK/Bx/zO275POwtqGJ0QRpC/ASEEN8wewv6S+j5ZLO6uie8ttGPizgVWoh2vfmKsDgWklC9LKbOklFlxcXHm8IsmJdPQ2mH2gDMaJQ9+tJuDpfWMiA/l9FFxvHZjFn+9YhJT0yP52qygqsjKiMLP4ENiRCDPXDWZ204bxsxhMbx6w3TqW9q55Y3NNLdpim/5vlKyhkRx5ph4JqdF8uqaI1Q3trGroIY31x/l1je2kF1UxzUz0uy+rAALJydTXNvC3uI65o7o2pjFhwcyPiWcH/d37fnkltXzq8XbeeDDXSx6eQML/rHabq/ry13FRAb78Ut97uenszNIiQziSQeutJ9uL2RKeiSPXDyO354/FoOP6GJmPFBST0NrB1kZjhWUEFqlz69uMk/8jrMzggKTgtJMPV/tLmFsUrh5BDY2KYyUyCCz4jKNxjKTuiq7iyYlc6Si0eyq29DawS2vb6alrZP/3TTdbH6aNyqOioZWs6lw/aFKSupauMxCSQCEBWreoT/sLzObeeta2vl0eyFXZaWyVjcxvXvrTACe/yGXgupmrpmRxsMXOrZKXzMjHQn85JUNdHQaef6aKfj7+tjESSOnrKHLOpa9xXW8teEYCycls+qBM1j74Jk8dsl4dhXUWpmaqxvbWL63jEsmp5jvfcaYeEL8DXxpMzKTUrLuUCVzhmtmxriwAC6YkMRHWwu6vFOldS0crWxilh3zngnTbhLlDa3UtXQwwo6CCvE34GcQVDe1c7SyifZOaVZkWkOZwaHyRiuz1OqcCtKigxgSYz1qn5IehRBYPavVuRUU1jRzzYx0c5ipjlne80h5I+kxwWbTo4lzxyWy6WgVVY3Wc5Xvb8knKSKQU/R7JUcGcVVWGldMS+XCiclOj7CfNiSKU0fG8uLKQ5TVt1BQ3UxSRKCV7KnpUVyVlcpra46QU3q8Od6eX0N2UR0PX5jJ1PTj9U7rqFkrqPWHKxkWG0KCrvgATh8VR2mdNo1hQkrJzvwaJqUdr5cLJ6cQHujLmw46J73BXQU1Qkr5MNCo72J+ATDT46lyn0IgzeL/VD3MJeaOiOHUkbE89fV+8qua+GxnITvya/jd+WN54bppPHP1ZM4co/XOF4xPJLuojp35NRworbc76QvaXMe/rp1KdlEdj36xl/yqJvaX1HNOZgJCCG47dRhHK5uY8tgyLv7XWv7wWTb7S+q4cU4GV2al2b0naOaeYN0EZE9BAZw5JoFtedVmW72Jz3YU4SPg0zvn8o+rJ1NY08w/f7DudXV0GvnhQBlnjo43V4JAPwO/Pnsk2UV1XXYs2FtUx/6Sei6bog1Y48MDOXNMPB9uLbAyC209pvWusoY4bqRAG9XkVzWRXaSd/TTMgTnQNILKLWtgR34Nl0457hUohOCczARW51TQ1NbB4YpG2jqM5vknSxaMS8TXR/D5Ls2G/tdv9nOwtJ7/XDeVUQnH3aFPG6V1aFYcKMNolLy06hDhgb7mtVmW3KjvL/fMMm0U9fHWAprbO7l+VoY5zpwRsXx/7zz2/Olclt1zOo9fMoGIIMe7hqVFB3PqyDha2o08dsl4u2bSCycmExrgy+JN1o4fX+4qxuAj+P2FmSRFaO7J188awpzhMXy1+/jo8ZPthbR1Grli2nGlG+hn4KyxmiORZXnmljVQ0WBtZvzpnAzqWzv4ZLt11TP1zmcOtV9X4Phi3Vz97Cd7CkoIYd7uyGRmH5lwPN6Fk5KICfHnf2uPApr35YbDlZwyIq7LvSKCNHf3LRYK6t2Nx4gJ8Teb6wCGx4WQFBHImtzj5t2jlY1238sLJibRaZRWZsbi2mZW5ZRzxbRUu6NHV/j9BZk0t3fy68U7OFbZRJreEbPkNwvGEOxv4EkLU/yXu4rxN/hw/sQkq7ghAb4Mjwtlj66gOjqNbD5Sxazh1uVjeudXHrDMexN1LR1MSo00hwX5G7h6ehrfZJfYXWjfG9xVUCabUI0QYjwQAXStof3PZmCkEGKoEMIfWIR2dpVLCCF46vKJ+AjBve/v5C9fH2BiqrUJxcSCcVphP/rFXgCHCgq03ufP5w1n8aY8s/vxWWOPK7r7zx3Nb88fw4vXTWPFffNY/cAZPHLxOAL9unq4mQj29+X8CUlEBvsxKdX+6OLMMfEYJay0cKOVUvLZjiLmjohlclokl0xJ4YppWq/Lcv+7zUerqWlq5xzdXGbi4knJRAb7deklfbK9AF8fwQUWbuOLpqdR0dDKDxajuC3HqkkID7Ayi9gjLSqIgupmsotqGZMU3qWXaiI2zJ/KhjY+1D3OLplibdE9JzOB1g4jq3MqzHOBmUldn1dUiD+njIzly13F7Miv4a0Nx/jp7AxOHWndqMWFBTAhJYIVB8p5Y/1RVudUcN+5o+2WVUiALz87fRircyrYfLSKtzfmMSk1ggk25WXwEVZzDt3x+MLxPHPVpC6jNku5F09O5svdRWZHFW1T02LmDI8hWjeRmbhwYhLHKrXOgJSSdzflMSktsosiP39CEtVN7VZmPpNn2xwLL1KTme1NGzPb4k15xIcF2O0gmIgPD6CsvsV8OKE9BQXo2x21kaOvORoedzxegK+B62cP4Yf9ZeSU1rMzv4aG1g5OtWM2BZiWEcX2Y9V0GiUltS0s31fGFdNSrUamQgjmjohl3aFKOo0So1FypKKRjJiuCmpsUjhZQ6J4Z2Oe2RT81vpjSImV0neX0YlhPLZwPOsPV7Ijv8ZuHYoJDeCWU4aZ8240Sr7cVcxpo+IID+za8ZmQEmEeQW3Lq6G+taNLW5YYEciYxDBWWTiQ7SqoAWCihYICuHHuUAJ8fXj4sz0enYtyV0G9LISIAh5GUwB7gb96LDU9RErZAdyF5mG4D3hfSumWb3RKZBAPXziWTUerKKlr4Y8XZXZZrwGQHhNMZlI4W49VE+RnYKIDJWHinnNGMSU9ktU5FYyIDzX3fA0+gjvPGMHtpw1nwfhEMmJDHJr1bHnk4nF8ftcpDhvviSkRxIb6WymI7fk15FU1mSf0AR5YMJoAXwOP6coWtEWu/r4+5t6TiUA/A1dnpbFsX6l5v7tOo6b05o2Ot2r8Th8VR0J4AO9ZuHBvOVpN1pDobvOYFh1MQ2sH2/NqGOekQYsLDaCt08iSzXnMGxVnNsWZmDE0mvBAX5bvLWVvcR3+vj4Mi7M/GrtwYjIF1c3c8dYW4kIDuFffuNaWeaPj2JZXzZNf7+fMMfFc78AbEeD6WRnEhgZw95Id5JY1OPRcdIf0mGCHysnENdPTaWk38qk+itldWEteVZPddWfzMxMx+Ai+2FXMlmPVWjotzFsm5o2OI8TfYLVDx/pDlaREBpmXBoDWmN84J4OcsgbzKGrj4Uo2HK7iZ6cPdzqCSAgPpKyulZyyBkIDfM1zLLZE6jua55Q1kBoVZLUjBcD1s4YQ4OvDf1cfZrW+kH3OcPudyGnpUdS3drC/pI57P9iBr4/g2pld83/qyFhqmtrJLqqluK6F1g6jlQefJdfNGsKRikbWHtLMha+uOcLCycldTIzucmVWmnkO3PKZW3L9bC3vr6w+wra8akrqWrhoUpLduONTIiita6WsvoWnvztAjD7vaMvpo+LYfKTa7EyyI7+GQD8fRiVYdyBSIoO4/9zRrDhQzqc7PLPMBdz34ntFSlktpVwppRwmpYyXUr7osdT0AinlV1LKUVLK4VLKP/fkHlfpL8Htpw1jmhNT1ILxmgnANP/kDD+DD88vmkJksB8XTrT/srhLaICvw5cUNA+100fFs+JAmXlbmKU7ivD39eHc8cfNF/FhgfzqrBH8sL+MT7YXIKVk2b4S5g6P6VLxQat8RinN3mTrDmkeX5dNtR69+Bp8uHJaGisOlHGwtJ7i2mYKa5qdOkiYMM0jtXYYnSoo01oobfK5a6PtZ/DhjDHx/LC/jN0FtYxJDHNYVvPHJeBv8KG0rpU/XjSOMDs9TtAaaqOE8EBf/nrFRKfKNsjfwC/mDaewppnwQF+7CqIvmJAawdT0SJ7+7gCHyxv4YlcxfgZhZbYyERXiz9wRsXy1u5h3N+YRFuDLhXYaNJOZ75s9JXR0GjEaJesPV9pt+BdOTiFrSBSPLM2mtK6F53/IITY0wG7Db0l8WACtHUa259UwPM5xZy0qWDsTKseBI0VMaABXZqXy6fYivthVxMTUSCKD/e3cCfN86N1LdrA2t5LHFo63q0jmjohFCPjnD7ns1+cgHXminjchkegQf95af4y/6Oa2BxaMcZp3V3l04XhumD2E88Z3LUuA6BB/rpiWyifbC/nfuqME+PqYLTa2mBwlnl2Ww8YjVdx9zii7o/nTR8XRpptKQfPgm5ASYbdz/NPZGUxNj+RPn+/t8ltPcdeLL0EI8aoQ4mv9/0whxC0eS80AI4Tgr1dM4rfnj3Uaz/SCWJo3nJEWHcy6B8/kV2eO7HUaXeWssfHUtXSwLa+Gjk4jX+wq4qwx8V2G+zfOGcrModH85sPdvLXhGPlVzZyTab8CpEUHc8boeBZvyuf9zfk89PFuwgJ97fa8bpybQVSwP3cv2WGet3LmIHFcxnHzhSMHCTiuoKKC/czzg7acPTaBysY2NhyptOsgYSI80I9FM9JYODmZ8yfYzzvA5LQoLpuSwvPXTDHLd8a1M9PJiAnm+tlD7C5M7iueWzQFX4MPt76xhS92FnHqyDgigu0r3QsmJJJX1cRnOwq5ZEqKwwl7k5nv422FZBfVUdvczpwRXRWUwUfwtysn0dZp5IbXNrE2t5KfnT7MqdkaMHtC7imqZYTNVkiWRAVrpt1D5cc9+Gy59ZRhtBuNHCpv5FQH87QA6dHBxIb6k1PWwFVZqVw13f7cb2xoAL87fyzL95Xy6yU7ABgWa98EGeCrzccs31fK0p1F3H7aMFIinZu1XSXQz8CfFo53mG+AW04ZSrvRyJe7ijljdLxDE7LJUWLxpjyGxYWwyEHep2VEERnsxzPLDlLf0k52UV0X854Jg4/gL5dPpKnV/hZNPcFdE9/raGY0U3fwIHC3x1JzgjAyIYzFt83ixjkZLl8T7O9r12TYV5wyMhZfH8F9H+zk+lc3UdHQZt7LzhJ/Xx9evj6L9Jhg844RZ9uZ+Ddx/ewhVDS08sBHu4gM9uO/P82y2/jEhgbw5GUT2FusOYkE+RnMu5E7wzQyNPgIxiQ6roixYVqveKGFx5ktp4+Ow88gkBKn8x+g9U6fWzTF6ajI4CN45urJLndMAv0M/HDvPO6bP9ql+J4iLTqYF34ylbyqJopqW5yO3E1mPqPEynvNlnmj4xgSE8wDH+1i0cvrAZg9zP5zGBobwv3njmF/ibbgtrvRExxfrCul4/kn0Ex8lY1ttHUYHcbLiA1hgT5itOe2b0IIwdljE5iUFsmjC8c7Td+tpw7jhZ9Mo8NoJMTfYN79wh7X6h6X8WEB/Oz04U7v62mGxYVytj5qsjcaNhES4Gt29HhwwRiH1oUAXwPPXDWJvcV1/PS1TbR2GM27qNtjZEIYj1/q/Fm6g+sztBqxUsr3hRAPgTb3I4TwnLo8gZjtwK7tLYQH+vHgeWNYebCc8vpWpqZHMm90V28m0BaY/u/G6Vz6n3UMiw0h3oH9H+D0kXHcf+5oRieEcdbYeKcN+vxxiVyVlcr7WwqYMzymW3OoKd0RQX4khgc67XWPjA/jzjOGc52TuZ3wQD9mDYthdU6F0xFUX9KfnRJLZg6L4cnLJvD2xrwuDi+WRIX4c87YBGqb250q8UA/A9/efRrf7Cnh3U152jxRhOP35KY5GRytaGT28BinbtQm4i0afGcKKspiJGjPxGfigQVjSIoI6tas/ORlE5DStXJaMD6Rz+NOobyh1el7nxYdzKMXj2N4XKhdU3lfc9/80QT5GTjLgWXBxEWTkskpbXD6foDmFXzvOaP4+3eaV6oj5ywTV2WlcbV7SXaIcMfjQgixArgcWCalnCqEmAX8RUp5uofS029kZWXJLVvUWYuW1Da1Y5TSvCDSEzS0dvCT/27gqulp/GSma44C976/k7ToIO4+276zgjt8tqOQRz/fy6oHzhiQxuJEoKPTiASXOhB9RX1LOxMe+Q6AH++b53CO5/3N+Tzw0S4Adj8y3+F8ocKzSCm5a/F2duTVsOY3Z3Tr7CSE2CqlzOqtXHdr7D1o3nvDhRBrgTjgit4mQuEdOJqn6A2hAb58dtcpbl3z9FWTPCZ/4eQULp6U7LKH5GDEkTdofxIa4EuQn4FOozTvyWgP03ZHyRGBSjn1I0II/rloCq0dxn6tSy69mUKI6UKIRCnlNuB04LdAK/Ad2rZCCoXXopST9yOEICE8gGFxIU4Vpml0P8KJo4Cib/DxEf3q7AOuO0m8BJi2JZgD/A5t9/Bq4OU+SJdCoRhkXDw5xe7ieEtMc1DO5p8UJw+umvgMUkrTToBXAy9LKT8CPhJC7OiTlCkUikHFPed0P+eYGBFEeKAvM4c63zJLcXLgsoISQvjqOzacBdzeg3soFApFrwgN8GXHH+YPmHekon9xVbksBlYKISqAZmA1gBBiBODaecNextatWyuEEJ7YfjcW8MxZ2yeefJX3gUPJV2XvzfJ7v7cXbriZ6y7lScB3UspGPWwUEKo7TwxKhBBbPOFOeSLKV3kfnHkf7PIHc977W77L5jkp5QY7Ye6fba1QKBQKhQsM/AIIhUKhUCjsoBRU7xloN/uBlK/yruQPRvmDOe/9Kt+trY4UCoVCoegv1AhKoVAoFF6JUlAKhUKh8E6klIPqA6QBP6IdV58N/FoPjwaWATn63yg9fAywHm3vwfts7rUAOADkAg86kXmDft8c4P9s5aNtwLuvH+Qf0eXs1f8WADuA3WhbWVX3gexvgBrgC5tnfxA4ql//CbDczby/BpQBe7opb1M6jwKHLJ77h7psCazoY9m5wJN2yv15oKEH5d5b+Y0WZV+s36sv5Jvj2ZT9fv2To4f96Kp8HNTf/ij7Hsr2WNl7KO+HOF7nWyzS4EnZdt8PHLSxTt+h7iKcbB+0tVxT9e9haA1lJvBX9IYWeBDtGBGAeGA68GebAjPohT0M8Ad2Apl25EUDh/W/UcAx4HQL+UVojXd5P8gfg9YwRdnJewFwmidl63HPAi7S82j57D9Gaxwz9Qrziat51387DZhqWwls4limM90iv2F6OZyNttD80T6W7Y9WqS+3KPdjaB2TNnfK3YPyTc/iAPCep+XbxrMp+2fR3vdMYCWwwo333m797Y+y76Fsj5W9h/JuWed3Az/1pGxn7wcO2lhnn0Fn4pNSFkt9YbGUsh5tJJECLATe0KO9AVyixymTUm4G2m1uNQPIlVIellK2AUv0e9hyLtr5WVVSymq0EYXpRGIJBKCNIML7Qf5+4EtggU3erwAE2g4hnpSNlPJ7oF7/Xiyl3Ca07cVPAzbr8qOASDfyjpRyFVBlG+4knXnA/4CFet536HkOBt7rY9ltwNuAabO5Jl3uO2hrEd0p917Ltyj7kcAI4Pd9IN8qnmW9A84HtqKVfQpg2iG2W/lO6q+z/Huk7Hso22Nl76G8m665FG1U9KmHZTt7P+y2sc4YdArKEiFEBjAF2AgkSCmL9Z9KAOfHTGqFk2/xfwH2C8xZvOcAoy7ftz/l2+Q9CVgsta6NJ2U7IgbNtDBZlx+JdrYYLsp3FVfybgBK+0u2/v0PaI3T12ietO6Ue6/lW+Q/AeiUUub0gXxnJKH14jeibZtjOpfdLfk25WhLn5Z9T2Tr3z1S9h7IezKwXEpZ52HZznC3jR28CkoIEQp8BNytF5IZvaGWfSx/NtrpxHegNdb9Kd8f67z7o+232C95B0KARAbm2fsxsOU+ArgXuG2A5Jvfe7RetLmn3I/yg7F+/tJd+c7qrxM8UvY9lO2xsvdQ3ntU53uad1tclTkoFZQQwlRY70gpP9aDS4UQSfrvSWiTfM4oRBsim0gFCoUQM4UQO/TPxQ7ilQCvopkZngHWAD5CiHX9ID8d7ciUd6SUHwshJgGdaHNhnpbdBf3Zvwx0oNnhQXOiKHdDvl2EEGkW8n9mJ53pwBlYl3snek+uj2Wnos25vYc2av67EOKofu2RfpT/EZp5aRWaKajQzffeVfn24pjqXRXaRDxom45WuiPfXv3tr7LvoWyPlb0n8i6EiEVr+7f2gWxnuNvGDr6jMvT5j1eBfVLKZyx+Worm7faU/vezbm61GRgphBiK9kIsAq6VUmajma5M8qKBJ4QQUXrQfCACbdPdu/U4Gfr9TA12X8pfBLxtkfdr9Ht5PO+2WD57oA5t7msJmvegaVd8V+TbRUqZj3XefW3SeS/whU25N6Ep7D/0sexFaB5Vq6WU0yzitaEdCEo/yd8npXxGb0y+QGsk3Cl7l+TbYlP2uyxkFlpE61a+o/rbH2XfC9keKXsP5v0KtHJYhIvl7qrsbnC3jR2UXnynoA0td6FNlu5Am7SNAb5Hc4FcDkTr8RPRbLh1aD39AiBc/+18NG+WQ8DvnMi8Gc3dNBd4wo78G9FemL6WX2BHdjEwqw9lr0YbHTWjNYYm+SZ35yK0F/UHN+Uv1tPeroff4kC+KZ2FNnkvQOu9d6C51Nb2oexDaKNGe+9dQw+evSfkNwAP07P33lX5lvEsy343muNMIZoX30pX5eOg/vZH2fdQtsfK3kN536F/v7IPZdt9P3Dwrjn7qK2OFAqFQuGVDMo5KIVCoVB4P0pBKRQKhcIrUQpKoVAoFF6JUlAKhUKh8EqUglIoFAqFV6IUlEKhUCi8EqWgFAqFQuGVKAWlUCgUCq9EKSiFQqFQeCVKQSkUCoXCK1EKSqFQKBReiVJQCoVCofBKlIJSKBQKhVcy6M6DMhEbGyszMjIGOhmKfqCmqZ3CmmaMFjv3+whBSmQQkcF+A5gyheLkZOvWrRVSyrje3mfQKqiMjAy2bNky0MlQ9ANzn/qBjprmLuEJkUGsffDMAUiRQnFyI4Q45on7KBOf4qSnyI5ychauUCi8g0E7glIMHpIjgyi0o4ySI4M8LuvT7YX87dsDFNU0kxwZxP3njuaSKSkel6NQDAbUCEpx0nP/uaMJ8jNYhQX5Gbj/3NEelfPp9kIe+ng3hTXNSKCwppmHPt7Np9sLPSpHoRgsDKiCEkIcFULsFkLsEEJs0cOihRDLhBA5+t8oPVwIIZ4XQuQKIXYJIaZa3OcGPX6OEOKGgcqPwju5ZEoKT142gZTIIASQEhnEk5dN8PjI5m/fHqC5vdMqrLm9k799e8CjchSKwYI3mPjOkFJWWPz/IPC9lPIpIcSD+v+/Ac4DRuqfmcALwEwhRDTwRyALkMBWIcRSKWV1f2ZC4d1cMiWlRwrJHZOdmutSKDyL0xGUPppx+OmjNC0E3tC/vwFcYhH+ptTYAEQKIZKAc4FlUsoqXSktAxb0UdoUJzifbi9k7lM/MPTBL5n71A9OzW/umuwczWlJ6FaWQqHoSncmvq3AFv1vOXAQyNG/b/WAfAl8J4TYKoS4XQ9LkFIW699LgAT9ewqQb3FtgR7mKLwLQojbhRBbhBBbysvLPZB8xYmEuwrHXZOdvbkuE2o+SqFwH6cmPinlUAAhxH+BT6SUX+n/n8fxkU1vOEVKWSiEiAeWCSH228iXQgjp4Fq3kVK+DLwMkJWV5bH7Kk4MnCmcS6akdDHn2fP8A8cmO5Pp72/fHrB7raUshULRPa46ScwyKScAKeXXwJzeCpdSFup/y4BPgBlAqW66Q/9bpkcvBNIsLk/VwxyFKxRWOJsjsje6Eg7u48w9/ZIpKax98EyH16r5KIXCdVxVUEVCiN8LITL0z++Aot4IFkKECCHCTN+B+cAeYClg8sS7AfhM/74U+KnuzTcLqNVNgd8C84UQUbrH33w9rF9xZ25DMTA4UizJkUF2R1cSuigaV93THcmKCPJT74lC4SKuKqhrgDi0Uc4nQLwe1hsSgDVCiJ3AJuBLKeU3wFPAOUKIHOBs/X+Ar4DDQC7wX+AXAFLKKuAxYLP+eVQP6zfU+hcNb1fSztZDORrZSOiRe7o9WX4+gsa2jl6/J97+nAcDqgz6ByHl4JyKycrKkr3di880Z+ForiLFhb3eTpadB0xK2nIUEuRn6JP1Rr3B0fOe+9QPdsvRsgzdLSvb+E1tHVQ3tTuV4Ur6T4TnfDLTmzI4Weo7OM+LEGKrlDKrtzKcKighxD+klHcLIT5H60xaIaW8uLcJGCh6q6DsvaS2CODIUxe4dY+eNDa9eek9VWFcaeC9me7KwhONkqOOTHfviSXe9JxPpsbWFXrbIT2ZOhfd5cVTCqq7hbpv6X//3ltBJxv25ixs6W6vN3e9ykxzH5ZhZ4yJ46Otheb7mMxGgEsNp+VL5u61vfF48zYsPfDsNbjdlZUjXOnIuLMnoLcsBu7Nu3Mi4ko5dlcGPanv9p7lQHRI7VkDelIf3MVtE5/uiJAmpdzlsVQMAL0dQQ198MuuQ0oLXOkZObtHsJ8PTe1GqzA/HwEC2juPXyWwM7TleG/O2QvpqDduEAKjlHZf4E+3F/LI0mxqmq1NVY7SIYDIYD9qmtpP6F62o7LqbvTj6Bmb6O496QszoSfwppFcf9BdOUL3eXdW3+3VnyA/A5dPS+HH/eUOO6SmeJYjfUf1vScjOEf13RlRwX7sfeFOWotzHDmzuoxLWx0JIVYAF+vxtwJlQoi1Usp7epuAExVno4YUFxtiZ/ewVU4A7caur7ejF97SddpRL9dRj69T77SY4m85VsWP+8vNrtf2ZJo83mx/k2BuUL2tl21psjEIQaeUDsuupzuiO+tVd/ee2Cs/Px+Bn0FYdVIsPQu7a6A81UP3lpFcf9Fdvlzx7nRW3+3Vqeb2Tt7ZkGf+rbCm2ep/y3imxePO6rujEdy97+/k/97b0cVK46y+O8NeB6qnuDSCEkJsl1JOEULcijZ6+qMQYpeUcqLHUtJLhBALgOcAA/CKlPIpZ/EtR1DOGiqwb/Zx1Bux7fG40wB5EgEIAXZ0GoA5n57GlfuaRlXVTe3m+JFBfgiBVZiz39yNb6sMnD17T/ZcezPScHRtZJAfIQG+Vmlz1IEw5eWLncVdesE9yWd3abPNl+VzifBAObr7LtjrBPSkvjsbQXXXubGU21f1vTv6qr47oviNuz0ygnJVQe1GW1/0BvA7KeVmb1JQQggD2jZM56BtdbQZuEZKudfRNSYF5eylsWdSc9Yo9aQRA7j7vR29yz/u93L6gpTIIIp0F2pvxLIsujPZOGro7XU+nJlOgB5PjLtiVnTVWcdV01J3JmMTrpiLBrJBtqQ7ZxcTzuo7dC3H7toHe3TnaHEiERnk59D0198K6krgYWCtlPLnQohhwN+klJf3NgGeQAgxG3hESnmu/v9DAFLKJx1dExYWJqdNm0b+lNvpDIhwT157Ez7Gdjr9wzG01RGVt4rQyv0O72VorSUqbxWVwxYgDX7H79PZTszhb6hOP83tNJixLD/R6/eh55jTIUF47zFjhtZa0ra/zNGZ97n9vEzX2uKs3NO2v0xDzBitjG3el+7o7r7O4ngcKYnN/cIq3d3lq9/S5gKmZ9bb+m4yZhva6pA+fhj9gh3KcvZ8evVspBzYum5Kg/bFbn33lIJyaQ5KSvkB8IHF/4cBr1BOOvY2jJ1pG0nfkPZ2gICAAAD9pXMP6RtEp9BezM6ACCqHLXB6r07/cKrTT7NSTgDS4EfFiAsRHc1g7AAfB8Xh6IX0phfVnA7hHelygKmMDG11bjcQzsrXWXho5X6XFJItjjo1UXmrupXtcYQwv+emvNjmqyFmjNbw+ocjOpqRvp4/sbinmJ5Tb+s7CHMZVIy40KGshpgxVmXXGRBBxYgLqRhxIYa2OoKqcmmMn9ilTdAEOqk/pvomjYDo/3rWz/Xd1RHUKLTzlxKklOOFEBOBi6WUj/dJqtxECHEFsEBKeav+//XATCnlXY6uMZn4XPHOcYUUfbLckV2+O9OXn48gNNCXmqZ2s63e5PnmyHNroDCZgVKcpM3kCRgR5EddS7vDubD+xtK70ZGpx1Uzl4neerT1ZE6kO9mu0FPTsKN5HW8w5znCVBb9Vd8d/WbC0mRsW+6upq+/TPvu1nch8JgXn6sKaiVwP/CSlHKKHrZHSjm+twnwBD0x8Y3InCQTfvpsjz1VuqQBePbqyQ4dJxZvzO92ktJRg+aJym/PXu7sN0fxo4L9+ONF48yNk6fmSfoDe3Mk9hSDq44CJnq7iNfZszE9b3DdWccVooL9uGBiUpd8uopt/jzV8PcFtsqgP+r72xvyur2Hux0eR+mwlxd367sjelLfwXM7Sbh6om6wlHKTsB7GdfRWuAfZDIwUQgxF28l8EXCtswsKa5rp0F8CRy7S7pAcGdTluAWDEF1cRZ3hyjEO3a2Hse3JWK4/sk1bdz11e2E9cb+2TX9feXO548VnSpcjBZI1JNrlBY3dLfJ1RncLvqub2rn/g51WDYo9d31X3g3oOvrJGhLdIycdk3uyKQ2uupdH9YH3ZndefLYdjr6o77aOUq7g6Jndf+5olzsdppGNszrd3/Xdk7g6gvoauAv4QEo5VTep3SKlPK9PUtUDhBDnA/9AczN/TUr5Z2fxA5JGyqQb/mEV1lMvNE95L52Ie7J5SzpOVLpb8O0MW3dzU4Pi6qjW1Bj5OHBBdqURN5W1K55pA7WI15kJ1hP1HXrmnefseXjrAm1X63t/j6DuRDvob4wQohA4Avykt8I9iX5e1VfdRnRCd9v22MNez9yVbZBscfUYBxO96bV7Em9Jx4mKu++bJTXN7WY3X8tRVXe9XNtGxp5ysjWLOcK0SLS7Xr+777cncbao2BP1vScd0u6eh+3o3pFi6O9n2t/13VUvvsPA2fq5TT5AE5oZ7VifpGqAMD1s2xfB3UlzV8wdjnq/7uDMRNWfeEs6+gtPbpLqjjmnO5wpC8vGzFEHytEWV901wEU1zQ7NuAO9xdWn2wsdjhA9Vd9d6ZD2tr57U0ewP+u7UwUlhAhHGz2loB0cuFz//15gF/BOXyewvzBVYGd2ZVd7L931yoL8DDxy8bhB1aifLHh6k1TbeUt7jaM7k9r2lIVtY+aoA2WU0u6+gqbr7n1/p8OG3hTPm95pU1k5GiF6qr67sg2SJ+q7tz3f/sCV3cyrgfXAbcDv0DoYl0opd/Rt0voPgxBWNlR7L4I7k+bOemWu7tOn8E56uqu5Le7umWeS3d2chCvKoicT3aZ7eYOZyVWcjRQ9Wd89sS+nwj7dKahhUsoJAEKIV4BiIF1K2dIboUKIR9AUXrke9Ft9DsnkIn4L0An8Skr5rR5ud6893XNvCRCDtpHt9VLKNnfSY5Sy2xfInd6LNw3HFZ7FE5ukdjcKc/SueWpOojsToCMcvdegOSJ427vubKToyfru6HkqR6He052CMnfRpJSdQoiC3ionC56VUlqdMyWEyESb2xoHJAPL9UXCAP/GYq89IcRSfa+9v+j3WiKEeBFNub3gTkL6wkVyMA7HBwOOessSrZF2pXH2xCisN52g3l7rTFF60471/eUSrTqkfUd3CmqSEKJO/y6AIP1/AUgppaf3WVkILJFStgJHhBC5wAz9t1zdWQMhxBJgoRBiH3Amx9c8vQE8ghsKyptNFArvw5lTg6uNs6eOquhNJ8hTHShPmTz7gp6OFHuC6pD2DU539ZRSGqSU4fonTErpa/G9t8rpLiHELiHEa/ohiGB/T70UJ+ExQI2UssMm3CVSIoPUMFzhFpdMSeHJyyaYt7OxxfJsHkc46sH31WLHvsSbz4WyLCuBqu8nIq6ug3IbIcRyINHOT79DG+E8hmYZeQx4Gri5r9JikSbzZrHp6ekn5cmfir7H1Ft2tCC2u8a5P3v2fU1/7yzgLmpkc2LTZwpKSnm2K/GEEP8FvtD/LQTSLH5O1cNwEF4JRAohfPVRlGV8e2l6GW3BMVlZWV6yfaniRKWnjfPJNGdxMilbhffh0lZHHhcqRJKUslj//n9oO48vEkKMA95Fm3dKBr4HRqLNeR0EzkJTQJuBa6WU2UKID4CPLJwkdkkp/+NCGsrxzELjWKDCA/c5EeUP6rz7BIUbfcPjhiAsDsSR0thRV37M2FxX1dfy8ZJn7xMUHm0IjU4RBl9/2dnR1tlQVXiS599rnr0Xyx8ipYzrraCBUlBvAZPRTHxHgTssFNbv0Mx9HcDdUsqv9XC7e+3phycuAaKB7cB1upNFf+Vliyf2nDoR5au8D868D3b5gznv/S2/z0x8zpBSXu/ktz8DXTZ6dbTXnu7ZN8M2XKFQKBQnNt57NrdCoVAoBjVKQfWelwexfJV3JX8wyh/Mee9X+QMyB6VQKBQKRXeoEZRCoVAovBKloBQKhULhnUgpB9UHbcHvj8BeIBv4tR4eDSwDcvS/UXr4GLTjRlqB+2zutQA4AOQCDzqReYN+3xzg/2zlA0uBff0g/4guZ6/+twDYAewG2tCOVvG07G+AGrTF2JbP/iDaEoNc4BO0s8bcyftrQBmwp5vyNqXzKHDI4rl/qMuWwIo+lp0LPGmn3J8HGnpQ7r2V32hR9sX6vfpCvjmeTdnv1z85etiPrsrHQf3tj7LvoWyPlb2H8n6I43W+xSINnpRt9/3AQRvr9B3qLsLJ9gGSgKn69zC0hjIT+Ct6Qws8CPxF/x4PTEdzfbcsMINe2MMAf2AnkGlHXjRwWP8bhbY4+HQL+UVojXd5P8gfg9YwRdnJewFwmidl63HPAi7S82j57D9Gaxwz9Qrziat51387DZhqWwls4limM90iv2F6OZwN1AKP9rFsf7RKfblFuR9D65i0uVPuHpRvehYHgPc8Ld82nk3ZP4v2vmcCK4EVbrz3dutvf5R9D2V7rOw9lHfLOr8b+KknZTt7P3DQxjr7DDoTn5SyWEq5Tf9ejzaSSEHbSf0NPdobwCV6nDIp5WYsjh7RmYG+w7rUzp9aot/DlnOBZVLKKillNdqIItmUHCAAbQQR3g/y9wNfAgts8n4F2m4dqz0sGynl90C9/r1YSrlNCCHQXuLNuvwoINKNvCOlXAV0t1uBZTrzgP8BC/W879DzHAy818ey24C3AdPRMU263HfQ1iK6U+69lm9R9iOBEcDv+0C+VTzLegecj3Z2m2kj6FRX5Tupv87y75Gy76Fsj5W9h/JuuuZStFHRpx6W7ez9sNvGOmPQKShLhBAZwBRgI5Ag9d0sgBIgoZvLHe2w7k685wCjLt+3P+Xb5D0JWCy1ro0nZTsiBs20MFmXHwmYtkVxRb6ruJJ3A1DaX7L1739Aa5y+RvOkdafcey3fIv8JQKeUMqcP5DsjCa0XvxFt25zYnsi3KUdb+rTseyJb/+6RsvdA3pOB5VLKOg/Ldoa7bezgVVBCiFDgI7TtlOosf9MbatnH8mcDlwN3oDXW/SnfH+u8+wOL+0k2QAjaTvcD8ez9GNhyHwHcC9w2QPLN7z1aL9ryUNL+kh+M9fOX7sp3Vn+d4JGy76Fsj5W9h/Leozrf07zb4qrMQamghBCmwnpHSvmxHlwqhEjSf09Cm+Rzht2d14UQM4UQO/TPxQ7ilQCvopkZngHWAD5CiHX9ID8duNqUdyHEJKATbS7M07K7oD/7l9H2WlyqB9egzUm4Kt8uQog0C/k/s5POdOAMrMu9E70n18eyU9Hm3N5DGzX/XQhxVL/2SD/K/wjNvLQKzRRU6OZ776p8e3FM9a4KbSIetE1HK92Rb6/+9lfZ91C2x8reE3kXQsSitf1b+0C2M9xtYwdmL76BRJ//eBXYJ6V8xuKnpWjebk/pfz/r5labgZFCiKFoL8Qi9B3W0UxXJnnRwBMWhzLOByKA76SUd+txMvT7mRrsvpS/CHjbIu/X6PfyeN5tsXz2QB3a3NcSNO/BWjfybhcpZT7Wefe1See9wBc25d6EprD/0MeyF6F5VK2WUk6ziNcGvKT/2x/y90kpn9Ebky/QGgl3yt4l+bbYlP0uC5mWx+N0K99R/e2Psu+FbI+UvQfzfgVaOSzCxXJ3VXY3uNvGDkovvlPQhpa70CZLd6BN2sagHe+Rg+byHK3HT0Sz4dah9fQLgHD9t/PRvFkOAb9zIvNmNHfTXOAJO/JvRHth+lp+gR3ZxcCsPpS9Gm101IzWGJrkm9ydi9Be1B/clL9YT3u7Hn6LA/mmdBba5L0ArffegeZSW9uHsg+hjRrtvXcNPXj2npDfADxMz957V+VbxrMs+91ojjOFaF58K12Vj4P62x9l30PZHit7D+V9h/79yj6Ubff9wMG75uyjtjpSKBQKhVcyKOegFAqFQuH9KAWlUCgUCq9EKSiFQqFQeCVKQSkUCoXCK1EKSqFQKBReiVJQCoVCofBKlIJSKBQKhVfy/7mrWUaolo8IAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.seasonal import STL\n",
    "\n",
    "stl = STL(dff['Weekly_Sales'])\n",
    "res = stl.fit()\n",
    "fig = res.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "exponential_smoothing = ExponentialSmoothing(\n",
    "    dff['Weekly_Sales'],\n",
    "    seasonal_periods=52,\n",
    "    seasonal='add',\n",
    "    trend='add',\n",
    "    freq='W',\n",
    "    initialization_method='estimated',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/statsmodels/tsa/holtwinters/model.py:920: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "fit = exponential_smoothing.fit(\n",
    "    smoothing_level=0.15,\n",
    "    smoothing_trend=0.15,\n",
    "    smoothing_seasonal=0.15,\n",
    ")\n",
    "\n",
    "dff['HoltWinters'] = fit.fittedvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Dept</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>IsHoliday</th>\n",
       "      <th>HoltWinters</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-02-07</th>\n",
       "      <td>2.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>66106.91</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66530.118311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-14</th>\n",
       "      <td>2.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>68501.78</td>\n",
       "      <td>1.0</td>\n",
       "      <td>68015.035701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-21</th>\n",
       "      <td>2.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>61551.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63780.931001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-28</th>\n",
       "      <td>2.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>57055.68</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54744.440524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-07</th>\n",
       "      <td>2.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>61029.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60194.425420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-30</th>\n",
       "      <td>2.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>67980.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71149.359045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-07</th>\n",
       "      <td>2.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>80830.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82504.327306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-14</th>\n",
       "      <td>2.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>84581.61</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82131.210068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-21</th>\n",
       "      <td>2.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75948.41</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75648.216142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-28</th>\n",
       "      <td>2.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>71383.59</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71346.193951</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Store  Dept  Weekly_Sales  IsHoliday   HoltWinters\n",
       "Date                                                          \n",
       "2010-02-07    2.0  93.0      66106.91        0.0  66530.118311\n",
       "2010-02-14    2.0  93.0      68501.78        1.0  68015.035701\n",
       "2010-02-21    2.0  93.0      61551.04        0.0  63780.931001\n",
       "2010-02-28    2.0  93.0      57055.68        0.0  54744.440524\n",
       "2010-03-07    2.0  93.0      61029.14        0.0  60194.425420\n",
       "...           ...   ...           ...        ...           ...\n",
       "2012-09-30    2.0  93.0      67980.56        0.0  71149.359045\n",
       "2012-10-07    2.0  93.0      80830.06        0.0  82504.327306\n",
       "2012-10-14    2.0  93.0      84581.61        0.0  82131.210068\n",
       "2012-10-21    2.0  93.0      75948.41        0.0  75648.216142\n",
       "2012-10-28    2.0  93.0      71383.59        0.0  71346.193951\n",
       "\n",
       "[143 rows x 5 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dff['Weekly_Sales'].plot(marker='o', color='black', legend=True)\n",
    "dff['HoltWinters'].plot(color='blue', legend=True)\n",
    "#ffcast.plot(marker='o', color='green', legend=True)\n",
    "\n",
    "\n",
    "ax = dff['Weekly_Sales'].plot(\n",
    "    figsize=(10, 6),\n",
    "    marker=\"o\",\n",
    "    color=\"black\",\n",
    "    title=\"Forecasts from Holt-Winters'  method\",\n",
    ")\n",
    "ax.set_ylabel(\"Weekly Sales\")\n",
    "ax.set_xlabel(\"Year\")\n",
    "fit.fittedvalues.plot(ax=ax, style=\"--\", color=\"red\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:132: FutureWarning: The 'freq' argument in Timestamp is deprecated and will be removed in a future version.\n",
      "  date_key = Timestamp(key, freq=base_index.freq)\n",
      "/opt/conda/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:132: FutureWarning: The 'freq' argument in Timestamp is deprecated and will be removed in a future version.\n",
      "  date_key = Timestamp(key, freq=base_index.freq)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure 7.6: Forecasting Holt-Winters method with both additive and multiplicative seasonality.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Additive</th>\n",
       "      <th>Multiplicative</th>\n",
       "      <th>Additive Dam</th>\n",
       "      <th>Multiplica Dam</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>$\\alpha$</th>\n",
       "      <td>1.464286e-01</td>\n",
       "      <td>1.464286e-01</td>\n",
       "      <td>1.110714e-01</td>\n",
       "      <td>1.110714e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$\\beta$</th>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>7.404762e-02</td>\n",
       "      <td>7.404762e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$\\phi$</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.900000e-01</td>\n",
       "      <td>9.900000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$\\gamma$</th>\n",
       "      <td>2.298077e-01</td>\n",
       "      <td>2.298077e-01</td>\n",
       "      <td>2.304630e-01</td>\n",
       "      <td>2.304630e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$l_0$</th>\n",
       "      <td>2.062166e+00</td>\n",
       "      <td>2.062166e+00</td>\n",
       "      <td>2.062157e+00</td>\n",
       "      <td>2.062131e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$b_0$</th>\n",
       "      <td>7.925300e-06</td>\n",
       "      <td>7.923495e-06</td>\n",
       "      <td>2.496683e-05</td>\n",
       "      <td>6.056266e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSE</th>\n",
       "      <td>2.311777e+09</td>\n",
       "      <td>2.310021e+09</td>\n",
       "      <td>2.310866e+09</td>\n",
       "      <td>2.275971e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Additive  Multiplicative  Additive Dam  Multiplica Dam\n",
       "$\\alpha$  1.464286e-01    1.464286e-01  1.110714e-01    1.110714e-01\n",
       "$\\beta$   1.000000e-04    1.000000e-04  7.404762e-02    7.404762e-02\n",
       "$\\phi$             NaN             NaN  9.900000e-01    9.900000e-01\n",
       "$\\gamma$  2.298077e-01    2.298077e-01  2.304630e-01    2.304630e-01\n",
       "$l_0$     2.062166e+00    2.062166e+00  2.062157e+00    2.062131e+00\n",
       "$b_0$     7.925300e-06    7.923495e-06  2.496683e-05    6.056266e-05\n",
       "SSE       2.311777e+09    2.310021e+09  2.310866e+09    2.275971e+09"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aust = dff['Weekly_Sales']\n",
    "seasonal_periods = 52\n",
    "fit1 = ExponentialSmoothing(\n",
    "    aust,\n",
    "    seasonal_periods=seasonal_periods,\n",
    "    trend=\"add\",\n",
    "    seasonal=\"add\",\n",
    "    use_boxcox=True,\n",
    "    initialization_method=\"estimated\",\n",
    ").fit()\n",
    "fit2 = ExponentialSmoothing(\n",
    "    aust,\n",
    "    seasonal_periods=seasonal_periods,\n",
    "    trend=\"add\",\n",
    "    seasonal=\"mul\",\n",
    "    use_boxcox=True,\n",
    "    initialization_method=\"estimated\",\n",
    ").fit()\n",
    "fit3 = ExponentialSmoothing(\n",
    "    aust,\n",
    "    seasonal_periods=seasonal_periods,\n",
    "    trend=\"add\",\n",
    "    seasonal=\"add\",\n",
    "    damped_trend=True,\n",
    "    use_boxcox=True,\n",
    "    initialization_method=\"estimated\",\n",
    ").fit()\n",
    "fit4 = ExponentialSmoothing(\n",
    "    aust,\n",
    "    seasonal_periods=seasonal_periods,\n",
    "    trend=\"add\",\n",
    "    seasonal=\"mul\",\n",
    "    damped_trend=True,\n",
    "    use_boxcox=True,\n",
    "    initialization_method=\"estimated\",\n",
    ").fit()\n",
    "results = pd.DataFrame(index=[\n",
    "    r\"$\\alpha$\", r\"$\\beta$\", r\"$\\phi$\", r\"$\\gamma$\", r\"$l_0$\", \"$b_0$\", \"SSE\"\n",
    "])\n",
    "params = [\n",
    "    \"smoothing_level\",\n",
    "    \"smoothing_trend\",\n",
    "    \"damping_trend\",\n",
    "    \"smoothing_seasonal\",\n",
    "    \"initial_level\",\n",
    "    \"initial_trend\",\n",
    "]\n",
    "results[\"Additive\"] = [fit1.params[p] for p in params] + [fit1.sse]\n",
    "results[\"Multiplicative\"] = [fit2.params[p] for p in params] + [fit2.sse]\n",
    "results[\"Additive Dam\"] = [fit3.params[p] for p in params] + [fit3.sse]\n",
    "results[\"Multiplica Dam\"] = [fit4.params[p] for p in params] + [fit4.sse]\n",
    "\n",
    "ax = aust.plot(\n",
    "    figsize=(10, 6),\n",
    "    marker=\"o\",\n",
    "    color=\"black\",\n",
    "    title=\"Forecasts from Holt-Winters' multiplicative method\",\n",
    ")\n",
    "ax.set_ylabel(\"Weekly Sales\")\n",
    "ax.set_xlabel(\"Year\")\n",
    "fit1.fittedvalues.plot(ax=ax, style=\"--\", color=\"red\")\n",
    "fit2.fittedvalues.plot(ax=ax, style=\"--\", color=\"green\")\n",
    "\n",
    "fit1.forecast(8).rename(\"Holt-Winters (add-add-seasonal)\").plot(ax=ax,\n",
    "                                                                style=\"--\",\n",
    "                                                                marker=\"o\",\n",
    "                                                                color=\"red\",\n",
    "                                                                legend=True)\n",
    "fit2.forecast(8).rename(\"Holt-Winters (add-mul-seasonal)\").plot(ax=ax,\n",
    "                                                                style=\"--\",\n",
    "                                                                marker=\"o\",\n",
    "                                                                color=\"green\",\n",
    "                                                                legend=True)\n",
    "\n",
    "plt.show()\n",
    "print(\n",
    "    \"Figure 7.6: Forecasting Holt-Winters method with both additive and multiplicative seasonality.\"\n",
    ")\n",
    "\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAD = eval_measures.meanabs\n",
    "MSE = eval_measures.mse\n",
    "\n",
    "\n",
    "def MAPE(y, y_hat, axis=0, zeros=np.nan):\n",
    "    \"\"\"\n",
    "    Mean Absolute Percentage Error\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    y : array_like\n",
    "      The actual value.\n",
    "    y_hat : array_like\n",
    "       The predicted value.\n",
    "    axis : int\n",
    "       Axis along which the summary statistic is calculated\n",
    "    zeros : float\n",
    "       Value to assign to error where y is zero\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    rmspe : ndarray or float\n",
    "       Root Mean Squared Percentage Error along given axis.\n",
    "    \"\"\"\n",
    "    y_hat = np.asarray(y_hat)\n",
    "    y = np.asarray(y)\n",
    "    error = y - y_hat\n",
    "    loc = y != 0\n",
    "    loc = loc.ravel()\n",
    "    percentage_error = np.full_like(error, zeros)\n",
    "    percentage_error.flat[loc] = error.flat[loc] / y.flat[loc]\n",
    "    mape = np.mean(np.abs(percentage_error)) * 100\n",
    "    return mape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2385.283888384232"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAD(dff['Weekly_Sales'], fit.fittedvalues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10262585.056169167"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MSE(dff['Weekly_Sales'], fit.fittedvalues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.366109419691665"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAPE(dff['Weekly_Sales'], fit.fittedvalues)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 2.13 (Forecasting Simulation) \n",
    "Consider a product whose daily demand follows 2.30 with $\\mu = 40$ and $\\sigma=6$.\n",
    "\n",
    "1. Build a spreadsheet simulation of the demand process, as well as a moving average forecast of order 5. Simulate the system for at least 500 periods. Report the MSE and MAD of the forecast. Also calculate the standard deviation of the forecast error. How accurate is the approximation given in 2.28 for your simulated values?\n",
    "2. Repeat part (a) for an exponential smoothing forecast with constant $\\alpha=0.1$.\n",
    "3. Based on the results of parts (a) and (b), does one forecasting method appear to work better than the other?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.13.1. Build a spreadsheet simulation of the demand process, as well as a moving average forecast of order 5. Simulate the system for at least 500 periods. Report the MSE and MAD of the forecast. Also calculate the standard deviation of the forecast error. How accurate is the approximation given in 2.28 for your simulated values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import pandas as pd\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "\n",
    "from statsmodels.tools.eval_measures import mse as MSE\n",
    "from statsmodels.tools. eval_measures import  meanabs as MAD\n",
    "\n",
    "def MA(endog, q, **args):\n",
    "    return ARIMA(endog, order=(0, 0, q), **args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "nsample = 500\n",
    "mu = 40\n",
    "sigma = 6\n",
    "e = np.random.normal(scale=sigma, size=nsample)\n",
    "idx = pd.date_range(\n",
    "    '2020-01-01',\n",
    "    periods=nsample,\n",
    "    freq='D',\n",
    "    name='Day',\n",
    ")\n",
    "df = pd.DataFrame(index=idx)\n",
    "df['Demand'] = 40 + e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "ma_mod5 = MA(df, 5).fit()\n",
    "fcast = ma_mod5.fittedvalues\n",
    "fcast.name = 'MA'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Demand'].plot(legend=True)\n",
    "fcast.plot(legend=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.901428660858537"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAD(df['Demand'], fcast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37.60386833135731"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MSE(df['Demand'], fcast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.160341749313178"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(ma_mod5.params['sigma2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.2572457123145995"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1.25*MAD(df['Demand'], fcast)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.13.2. Repeat part (a) for an exponential smoothing forecast with constant $\\alpha=0.1$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.api import SimpleExpSmoothing\n",
    "\n",
    "fit = SimpleExpSmoothing(df['Demand'], initialization_method=\"heuristic\").fit(\n",
    "    smoothing_level=0.1,\n",
    "    optimized=False,\n",
    ")\n",
    "\n",
    "fcast = fit.fittedvalues\n",
    "fcast.name = 'SES'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Demand'].plot(legend=True)\n",
    "fcast.plot(legend=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.1232041906637935"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAD(df['Demand'], fcast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40.57665024393054"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MSE(df['Demand'], fcast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.369980395882749"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(MSE(df['Demand'], fcast))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.4040052383297414"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1.25 * MAD(df['Demand'], fcast)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Based on the results of parts (a) and (b), does one forecasting method appear to work better than the other?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MAD and MSE are lower in (a), the forecast method appears to work better in this case"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.14 (Bass Diffusion for LPhone) \n",
    "HCT, an Asian manufacturer of a new 4G cell phone, the LPhone 5, is planing to enter the U.S. market, and they are in the process of signing a contract with a third‐party logistics (3PL) provider in which they must specify the size of the warehouse they want to rent from the 3PL. HCT wants to forecast the total sales of the LPhone 5, as well as the time at which the LPhone 5 reaches its peak sales. After some thorough market research, HCT has estimated that $p =0.008$ , $q =0.421$ , and $m=5.8$ million. Calculate when the peak sales will occur and how many LPhone 5 the company will have sold by that point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from pprint import pprint "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Bass:\n",
    "\n",
    "    def __init__(self, p, q, m):\n",
    "        self.p = p\n",
    "        self.q = q\n",
    "        self.m = m\n",
    "        self.params = {}\n",
    "\n",
    "    def time_peak(self):\n",
    "        self.params['time_peak'] = np.math.log(\n",
    "            self.q / self.p) / (self.p + self.q)\n",
    "\n",
    "    def demand_rate_at_peak(self):\n",
    "        self.params['demand_rate_at_peak'] = self.m * (self.p +\n",
    "                                                       self.q)**2 / (4 * self.q)\n",
    "\n",
    "    def cumulative_demand_at_peak(self):\n",
    "        self.params['cumulative_demand_at_peak'] = self.m * (\n",
    "            self.q - self.p) / (2 * self.q)\n",
    "\n",
    "    @staticmethod\n",
    "    def model_at_peak(p, q, m):\n",
    "        bass = Bass(p, q, m)\n",
    "\n",
    "        bass.time_peak()\n",
    "        bass.demand_rate_at_peak()\n",
    "        bass.cumulative_demand_at_peak()\n",
    "\n",
    "        pprint(bass.params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'cumulative_demand_at_peak': 2844893.111638955,\n",
      " 'demand_rate_at_peak': 633870.427553444,\n",
      " 'time_peak': 9.238208139866073}\n"
     ]
    }
   ],
   "source": [
    "p = 0.008\n",
    "q = 0.421\n",
    "m = 5.8 * 10**6\n",
    "\n",
    "\n",
    "#cumulative_demand_at_peaak =  #Lphon 5 sold at peak\n",
    "Bass.model_at_peak(p, q, m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.20 (Leading Indicators) \n",
    "A battery manufacturer produces a large number of models of lithium‐ion batteries for use in computers and other electronic devices. The products are introduced at different times and follow different demand processes. The company wishes to determine whether some of the products can serve as leading indicators for the rest of the products. The file batteries.xlsx contains historical demand data for 25 products for the past 26 weeks.\n",
    "Using Algorithm 2.1 with parameters images , images , and images , determine all pairs images such that product i is a leading indicator with lag k. (Note: You should not need to recluster the products.)\n",
    "Using one of the images you found in part (a), forecast the demand for the rest of the cluster in periods 27 and 28."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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